This paragraph makes me feel nostalgic, wishing I was in that era fluently communicating to everybody. I've always been fascinated with how communication use to be. Everything was said eloquently. I don't believe we're in a "talking era" in the 21st century, I even wonder if words are becoming less meaningful. What happened?

This year for the first time ever I started working with a larger team of people where we all must have a good line of communication to get our job done well. This was my first time in a job seeing how the lack of interpersonal skills could have a huge impact, not just on the two people taking but the entire team. It quickly became clear that whenever there was friction some part of communication wasn't being fulfilled. Rather differing opinions, negative talk around the work, or poor conflict resolution. In one way or another failing to meet eye to eye or failing to treat everyone with equal respect can quickly become a team rather than a two-person problem.

I never thought of self reflection or "talking to myself" as being a kind of communication that has anything to do with others. But as the text mentions in the paragraph above the way others communicate with us can then shape how we move on to the interact with the next person, It makes me wonder how many social behaviors people participate in that are an echo of a past experience they had with someone else. It makes the act of deliberate self-reflection just that much more important.

I never stopped to think what the first words might have actually been or sounded like. It reminds me a lot of talking with babies or toddlers. How sometimes they assign sounds or gestures to the things they ask for the most. I have even seen kids use a word that sound similar then pair it with hand gestures until someone understands. While it may be confusing to a stranger being around a baby or kid long enough you can start to see the connections and communicate with them easier.

I first came across this topic when talking to a friend visiting from another country. She wanted to know why in America we say hello to everyone and make small talk. My first impulse was to tell her it's just what you're supposed to do, and not to be rude. While the concept of different countries having different norms seems obvious in the moment, I was a little surprised she didn't do the same at home. Phatic communion was something so simple yet such a regular practice for me it feels like second nature. So, I find it very interesting to learn about what is considered phatic communion in other countries.

Remington Rand Portable 5 typewriter spool covers<br /> https://makerworld.com/en/models/762248-remington-rand-portable-5-typewriter-spool-covers#profileId-697034

via https://www.reddit.com/r/typewriters/comments/1tvv2md/3d_printed_spool_covers/

Growing up with a culturally diverse group of friends and diverse home was a gift. It allowed me to learn how to speak to those that weren't the same as me. It also taught me that there is usually some way to relate or connect to someone despite differences. I believe it's an exposure that has made it easier for me to build connections when I am in a new place. However, I have also seen firsthand how it's not always easy. Hiccups always happen but can be sorted if one person is willing to listen and the other is willing to explain. Unfortunately, I have even been around people where it seems impossible for them to even be interested in communicating with someone that doesn't familiar or comfortable to them.

does this translate to tangible outreach strategies and events? once this audience is established as a target, how can an academic library, for example, take measures to promote engagement?

This is a really meaningful connection to make. Oftentimes when folks connect universities to their racist origins, they're imagining the often invisible history of white supremacy that shapes curriculum, endowments, admissions, etc. It's important to also note that when racism drives an institution, it can also be direct and literal.

I'm curious about what this means in practice. When they say "describe" what system of logging this information are they referring to? Is this done through tags? I'm curious.

altijd naar beneden afronden, als dit in het voordeel van belastingplichtige komt

bepaalt in welke box een inkomstenbestanddeel wordt belast

bepaalde persoonlijke uitgaven die je van je inkomen mag aftrekken bij de inkomstenbelasting

extra belastingaanslag die de belastingdienst achteraf oplegt omdat blijkt dat eerder te weinig belasting is geheven

https://www.reddit.com/r/typewriters/comments/1tvhxka/snail_mail_pen_pal_clubs/

why can't it be considered marxist

Should be labeled as an early automated prioritization tool, outside of legal research.

https://docs.google.com/document/d/1B5bJw64Gc2oSPhlxwzkqyIqKenpPg1d_oApEidAxFew/edit?tab=t.0 This has too much sensitive comment. Make a simpler page with a list of papers considered and a brief discussion.

Claude: Deep-research update (June 2026) — new sources added on omnivore fraction of PBM consumers.

Three high-confidence new findings integrated:

1. GFI US Consumer Snapshot (Jan 2025, Morning Consult Dec 2024, n=3,079): 72% of past-year US PBM eaters are active meat-eaters (57% omnivore + 15% carnivore); only 11% veg*n/pescatarian. Retail panel corroboration: 87% of US retail PBM dollars come from meat-buying households ($970M of $1,118M, NielsenIQ Homescan 2023). Now shown as primary chart in §04.5, replacing the less-precise Hartman Group range.

2. Neuhofer & Lusk (2022, Scientific Reports — already at src-29) details added: 85.97% of PBMA-buying households also purchased conventional ground meat; only 2.79% were exclusive PBMA buyers. Peer-reviewed, IRI scanner panel, n=38,966 households — the strongest methodological data point in the set.

3. Bryant Research UK (July 2023, n=1,000, new src-48): Occasional UK PBM consumers ~67% omnivore (consistent with prior claims). Key nuance: frequent UK PBM buyers split ~1/3 each (omnivores, flexitarians, non-meat-eaters). The ≥50% arithmetic lower bound for weekly consumers still holds regardless of frequency segmentation, but the 70–80%+ claim is better supported for occasional buyers or all-buyers combined than for the most committed frequent buyers specifically. This caveat is now added to the §04.5 evidence table and §08.

Stat card updated: '≥50%' replaced by '72%' (US direct survey) as the headline figure, with UK lower bound in the tooltip. TLDR and §08 paragraphs updated accordingly.

that year, the Brazilians beat Germany in Yokohama, Japan

The eight nations shown in the panel chart, plus Switzerland as the extreme

Part of the early-1990s rise, therefore, reflects post-Cold-War mobility, not the Bosman ruling alone.

The World Cup squad data come from Wikipedia (1990–2022) and the Panini album (2026); players' nationalities and club affiliations come entirely from Transfermarkt. Consistency of player attributes across sources was checked on a sample basis but not systematically.

The national team's share of players abroad

All 14 nations contribute to the aggregate figure (Chart 01)

Argentina, Belgium, Brazil, Germany, England, France, Italy, Mexico, Netherlands, Portugal, Sweden, Spain, South Korea, and the USA

Only 22% of England's national team players play abroad. In Argentina, it is 94%.

The exceptions are, above all, the nations with the strongest leagues that can keep a share of their talent at home.

Lately, teams stacked with players abroad have done well, but before them, so did the Spanish and Italians who prefer to play in their home leagues.

Anyone following the 2026 World Cup is watching not only the best footballers in the world but also looking into a world where talent, dreams and shirts have long since stopped recognising borders.

The chart below shows this rise among the top players (who, in at least one of three criteria, ranked in the top 5% of their league in a season: goals per match, goals per minute played, or minutes played per match), and across full squads, the figures are practically the same.

(i.e., held a non-domestic nationality or were born abroad)

Football is becoming more global every year.

European clubs recruit far more than just superstars like Lionel Messi and Vinícius Júnior.

France, home to the world's fifth-strongest league, has a foreign-based share of 83% this year, a record for Les Bleus, and perhaps a recipe for success. After all, the French, led by Kylian Mbappé, lifted the trophy in 2018 and were beaten only on penalties by Argentina four years ago.

Bulgaria's World Cup heroes, Hristo Stoichkov and Yordan Lechkov, played for FC Barcelona and Hamburger SV, respectively, in 1994.

National leagues and UEFA's European competitions enforced the "3+2" rule, which limited each team on the pitch to no more than three foreign players, plus two "assimilated" players who had spent years in the country.

The sport and its labour market have grown more open and global. Since 1990, there has been a genuine migration of skilled labour: at the 1990 World Cup, 26% of national-team players were based at foreign clubs; at this year's tournament, the figure is 72%.

The shock was complete: in the summer of 1994, footballing minnow Bulgaria knocked out the favourites and defending champions, Germany, in the World Cup quarter-final in East Rutherford, New Jersey.

This metaphor compares Romeo’s sadness to a flower bud destroyed before it can bloom

MNT, SK

MNT

MNT

MNT

MNT, IS, SK

MNT, IS

We have the national Swedish Ethical Review Authority in Sweden who handles all ethics approvals.

The final version of this article already published in The American Journal of Pathology (PMID: 41763537 DOI: 10.1016/j.ajpath.2026.02.002).

Do all assignments need to have value outside of the course? Am I thinking too much about this?

• Informativo nº 651
• 2 de agosto de 2019.
• PRIMEIRA SEÇÃO
• Processo: REsp 1.164.893-SE, Rel. Min. Herman Benjamin, Primeira Seção, por unanimidade, julgado em 23/11/2016, DJe 01/07/2019

Ramo do Direito DIREITO ADMINISTRATIVO

Tema <br /> Loteamento. Regularização. Poder-dever municipal. Limitação às obras de infraestrutura essenciais. Cobrança do loteador dos custos da atuação saneadora.

DESTAQUE - Existe o poder-dever do Município de regularizar loteamentos clandestinos ou irregulares restrito às obras essenciais a serem implantadas em conformidade com a legislação urbanística local, sem prejuízo do também poder-dever da Administração de cobrar dos responsáveis os custos em que incorrer a sua atuação saneadora.

INFORMAÇÕES DO INTEIRO TEOR - De início, pontua-se ser encargo inafastável do Município promover a ocupação ordenada do solo urbano, consoante previsão do art. 30, VIII, da CF/1988. O dever de realizar o asfaltamento das vias, a implementação de iluminação pública, redes de energia, água e esgoto, calçamento de ruas, etc. refere-se a todo o território do ente político, e não apenas aos loteamentos incompletos, de modo a "garantir o bem-estar de seus habitantes", nos termos do Plano Diretor e da legislação urbanística, conforme o art. 182 da CF/1988, atendendo-se aos mais carentes em primeiro lugar.

• No âmbito infraconstitucional, a atuação do governo local deve buscar garantir o "direito a cidades sustentáveis" e evitar o parcelamento do solo inadequado em relação à infraestrutura urbana, segundo determina o art. 2º, I e VI, "c", do Estatuto da Cidade (Lei n. 10.257/2001). O dever de regularizar loteamentos há de ser interpretado à luz dessas disposições constitucionais e legais. Além disso, o art. 40, § 5º, da Lei n. 6.766/1979 (Lei do Parcelamento do Solo Urbano) determina que a regularização dos loteamentos deve observar as diretrizes fixadas pela legislação urbanística, sendo inviável impor ao Município descumprimento de suas próprias leis (quando, por exemplo, proíbe a ocupação de certas áreas de risco), por conta tão só de omissão do loteador.

• Evidentemente, ao Poder Judiciário não compete determinar a regularização de loteamentos clandestinos (não aprovados pelo Município) em terrenos que ofereçam perigo imediato para os moradores lá instalados ou mesmo fora do limite de expansão urbana fixada nos termos dos padrões de desenvolvimento local.

• A intervenção judicial, nessas circunstâncias, faz-se na linha de exigir do Poder Público a remoção das pessoas alojadas nesses lugares insalubres, impróprios ou inóspitos, assegurando-lhes habitação digna e segura, o verdadeiro direito à cidade. Mesmo na hipótese de loteamentos irregulares (aprovados, mas não inscritos ou executados adequadamente), a obrigação do Poder Público restringe-se à infraestrutura para sua inserção na malha urbana, como ruas, esgoto, iluminação pública etc., de modo a atender aos moradores já instalados, sem prejuízo do também poder-dever da Administração de cobrar dos responsáveis os custos em que incorrer na sua atuação saneadora.

• Registre-se que descabe impor ao Município o asfaltamento, por exemplo, de um condomínio de veraneio ou de classe média, se as ruas da cidade, que servem diariamente os moradores permanentes ou os em pobreza extrema, não possuem esse melhoramento.

• Inviável ainda obrigá-lo a implantar calçadas e vias em um condomínio de luxo, apenas porque o loteamento não foi completado, se o restante da cidade, onde moram os menos afortunados, não conta com iluminação pública ou esgotamento sanitário.
• Em síntese, o juiz dos fatos haverá, na apuração da responsabilidade estatal, de estar atento a esses conflitos para definir, entre as prioridades urbanístico-ambientais, o que é mais importante. Assim, não é possível afastar peremptoriamente a responsabilidade do Município, devendo este ser condenado a realizar somente as obras essenciais a serem implantadas, em conformidade com a legislação urbanística local (art. 40, § 5º, da Lei do Parcelamento do Solo Urbano).

• Informativo nº 543
• Período: 13 de agosto de 2014.

• TERCEIRA TURMA DIREITO PROCESSUAL CIVIL E DIREITO CIVIL. RECONHECIMENTO DA NULIDADE DO CONTRATO E SEU EFEITO SOBRE AÇÃO ORDINÁRIA DE RESOLUÇÃO DE PROMESSA DE COMPRA E VENDA DE IMÓVEL LOCALIZADO EM LOTEAMENTO IRREGULAR.

• Deve ser extinto sem resolução de mérito o processo decorrente do ajuizamento, por loteador, de ação ordinária com o intuito de, em razão da suposta inadimplência dos adquirentes do lote, rescindir contrato de promessa de compra e venda de imóvel urbano loteado sem o devido registro do respectivo parcelamento do solo, nos termos da Lei 6.766/1979. De fato, o art. 37, caput, da Lei 6.766/1979 (que dispõe sobre o parcelamento do solo urbano) determina que é "vedado vender ou prometer vender parcela de loteamento ou desmembramento não registrado".

• Além disso, verifica-se que o ordenamento jurídico exige do autor da ação de resolução do contrato de promessa de compra e venda a comprovação da regularidade do loteamento, parcelamento ou da incorporação, consoante prevê o art. 46 da Lei 6.766/1979: o "loteador não poderá fundamentar qualquer ação ou defesa na presente Lei sem apresentação dos registros e contratos a que ela se refere".

• Trata-se de exigência decorrente do princípio segundo o qual a validade dos atos jurídicos dependem de objeto lícito, de modo que a venda irregular de imóvel situado em loteamento não regularizado constitui ato jurídico com objeto ilícito, conforme afirmam a doutrina e a jurisprudência. Dessa forma, constatada a ilicitude do objeto do contrato em análise (promessa de compra e venda de imóvel loteado sem o devido registro do respectivo parcelamento do solo urbano), deve-se concluir pela sua nulidade.

• Por conseguinte, caracterizada a impossibilidade jurídica do pedido, o processo deve ser extinto sem resolução do mérito, nos termos do art. 267, VI, do CPC. REsp 1.304.370-SP, Rel. Min. Paulo de Tarso Sanseverino, julgado em 24/4/2014.

única hipótese de vedação absoluta, não comportando relativização através de correções ou compensações.

• Informativo nº 806
• 9 de abril de 2024.
• QUARTA TURMA
• Processo<br /> AgInt no REsp 2.015.374-SP, Rel. Ministro Antonio Carlos Ferreira, Rel. para acórdão Ministro Marco Buzzi, Quarta Turma, por maioria, julgado em 2/4/2024.

Ramo do Direito DIREITO CIVIL

TemaTrabalho decente e crescimento econômico Paz, Justiça e Instituições Eficazes <br /> Descumprimento contratual. Atraso na entrega de obra de imóvel. Lucros cessantes. Presunção. Impossibilidade. Finalidade do negócio, destinação do bem e prejuízos do comprador. Averiguação. Necessidade.

DESTAQUE - No caso de descumprimento contratual decorrente do atraso na entrega de imóvel, os lucros cessantes não são presumíveis, pois dependem da finalidade do negócio, destinação ou qualidade do bem (edificado ou não), bem como da demonstração do prejuízo direto do adquirente.

INFORMAÇÕES DO INTEIRO TEOR - A jurisprudência da Segunda Seção do STJ, firmada na sistemática dos recursos repetitivos, é de que, "no caso de descumprimento do prazo para a entrega do imóvel, incluído o período de tolerância, o prejuízo do comprador é presumido, consistente na injusta privação do uso do bem, a ensejar o pagamento de indenização, na forma de aluguel mensal, com base no valor locatício de imóvel assemelhado, com termo final na data da disponibilização da posse direta ao adquirente da unidade autônoma" (REsp n. 1.729.593/SP, Relator Ministro Marco Aurélio Bellizze, Segunda Seção, julgado em 25/9/2019, DJe 27/9/2019).

• Quando o atraso se dá na entrega de imóvel edificado, é possível vislumbrar, de antemão, independentemente da destinação do bem - residencial ou comercial - que a injusta privação do seu uso enseja o pagamento de lucros cessantes, pois seja para moradia própria, fixação de estabelecimento comercial ou auferimento de renda advinda da locação do bem, a utilização de parâmetro afeto a aluguel mensal de imóvel assemelhado mostra-se adequada à realidade atinente à qualidade do bem, pois o imóvel edificado está apto a servir a tais propósitos.

• A despeito de ocorrer a possibilidade de, eventualmente, em casos específicos, existir lucro cessante decorrente do atraso na entrega das obras de infraestrutura de terreno/lote não edificado, via de regra, é inviável, de plano, consignar tal encargo por presunção de prejuízo para toda e qualquer hipótese envolvendo referidos bens de modo a fazer incidir, ante a injusta privação do seu uso, o pagamento de indenização prontamente estabelecida na forma de aluguel mensal, com base em valor locatício de imóvel assemelhado.

• Considera-se imprescindível, para tal fim, averiguar ao menos a finalidade do negócio, a destinação e a qualidade do bem. Ademais, essa Corte tem entendimento pacífico no sentido de que em se tratando de imóvel não edificado, eventual inadimplência do comprador não enseja o pagamento de taxa de fruição justamente em razão de se tratar de terreno sem edificação, ante o princípio de não ter sido utilizado para qualquer fim.

• A premissa utilizada para tal compreensão pode ser analogicamente aplicada à questão envolvendo os lucros cessantes, já que, não sendo o terreno edificado, não é dado presumir que fosse utilizado para qualquer finalidade imediata, seja residencial, implementação de negócio, locatícia, entre outros, a autorizar a incidência de parâmetro vinculado a valor de aluguel mensal de bem assemelhado. Isso porque, fora casos muito específicos, não é comum que se proceda à locação de imóvel não edificado em loteamento, visto servirem os terrenos para construção futura de residência, implementação de negócio ou especulação imobiliária.

• A realidade posta a debate - vinculada a atraso na entrega de lote/terreno não edificado - demanda que se faça um distinguishing em relação ao entendimento sedimentado em recurso repetitivo - diga-se, específico para descumprimento do prazo de entrega de bem edificado - dada a expressa disposição da lei (arts. 402 e 403) segundo a qual os lucros cessantes representam aquilo que o credor razoavelmente deixou de lucrar, por efeito direto e imediato da inexecução da obrigação pelo devedor.

• Ora, caso o terreno servisse ao propósito de edificação futura para implementação de moradia ou negócio, é certo que tal não se daria imediatamente. Do mesmo modo, na hipótese de os lotes terem sido adquiridos para especulação imobiliária, o acréscimo patrimonial não se verificaria de plano, constituindo mera expectativa futura de ganho.

• Por tais razões, ainda que tenha havido descumprimento contratual decorrente do atraso na entrega do imóvel não edificado, os lucros cessantes não são passíveis de presunção, devendo ser devidamente demonstrados e cotejados para representar aquilo que o adquirente efetivamente deixou de lucrar em virtude do prejuízo direto e imediato do comportamento do devedor, afinal, nos lucros cessantes é imprescindível que se tenha certeza da vantagem perdida.

• REsp 2.105.387-SP, Rel. Ministro Gurgel de Faria, Primeira Turma, por unanimidade, julgado em 14/5/2024.

Ramo do Direito DIREITO TRIBUTÁRIO, DIREITO URBANÍSTICO

DESTAQUE - As providências elencadas no art. 53 da Lei n. 6.766/1979 para que possa ser alterado o uso de solo rural para fins urbanos, dentre elas a necessidade de prévia audiência do Incra, não configuram condição à caracterização do fato gerador e à cobrança de IPTU sobre imóvel que, por lei local, passou a integrar a zona urbana da municipalidade e que preenche os requisitos do art. 32 do CTN.

INFORMAÇÕES DO INTEIRO TEOR - Cinge-se a controvérsia em definir se o art. 53 da Lei n. 6.766/1979 estabelece a obrigação do município de previamente comunicar ao Instituto Nacional de Colonização e Reforma Agrária - Incra acerca da alteração de destinação de área rural para urbana, como condição para que a propriedade deixe de sofrer a incidênica do Imposto sobre a Propriedade Territorial Rural - ITR e passe a sofrer a incidência do Imposto Predial e Territorial Urbano - IPTU, a fim de se evitar a bitributação.

• O art. 182 da Constituição Federal (CF) preconiza que "a política de desenvolvimento urbano, executada pelo Poder Público municipal, conforme diretrizes gerais fixadas em lei, tem por objetivo ordenar o pleno desenvolvimento das funções sociais da cidade e garantir o bem-estar de seus habitantes".

• A Lei n. 6.766/1979, por sua vez, é a lei federal ordinária, recepcionada pela CF, que disciplina as normas gerais sobre a política urbana referente ao parcelamento do solo, dispondo no mencionado art. 53 que: "todas as alterações de uso do solo rural para fins urbanos dependerão de prévia audiência do Instituto Nacional de Colonização e Reforma Agrária - INCRA, do Órgão Metropolitano, se houver, onde se localiza o Município, e da aprovação da Prefeitura municipal, ou do Distrito Federal quando for o caso, segundo as exigências da legislação pertinente".

• Não há na redação do texto legal, portanto, passagem que possa sugerir eventual subordinação entre os entes públicos, notadamente em relação à existência de condicionante para fins de tributação municipal. As condições estabelecidas no supracitado dispositivo dizem respeito à realização de alterações no uso do solo rural para fins urbanos, ou seja, são dirigidas à pessoa do loteador, que somente poderá efetivar essa modificação de utilização da área depois de consultar ("prévia audiência") o Incra e o órgão municipal pertinente e de obter a aprovação do projeto pela prefeitura ou do Distrito Federal.

• Essa disposição legal atribui apenas à municipalidade a atribuição de aprovar ou desaprovar essa modificação de uso para fins de loteamento, o que guarda sintonia com o art. 12 dessa mesma lei, que assim dispõe: "Art. 12. O projeto de loteamento e desmembramento deverá ser aprovado pela Prefeitura Municipal, ou pelo Distrito Federal quando for o caso, a quem compete também a fixação das diretrizes a que aludem os arts. 6º e 7º desta Lei, salvo a exceção prevista no artigo seguinte". Ressalte-se ainda que essa consulta ao Incra está prevista antes da aprovação do projeto pela municipalidade e, por conseguinte, da lei municipal que integrará essa área na zona urbana da cidade.

• Constata-se, assim, que as providências elencadas no art. 53 da Lei n. 6.766/1979 dizem respeito às condições para se garantir, no máximo, a regularidade do processo de parcelamento/loteamento de área então rural, e não aos requisitos para a cobrança do IPTU sobre imóvel que, por lei local, passou a ser considerado como urbano, ou seja, o supracitado comando normativo trata de regra procedimental para fins de parcelamento do solo urbano, não implicando regra de natureza tributária.

• Ademais, eventual circunstância condicionante à configuração do fato gerador do tributo em questão (IPTU) somente poderia ser validamente instituída por lei complementar (art. 146, III, "a", da CF), o que não é o caso da Lei n. 6.766/1979; bem como não se vislumbra a ocorrência de bitributação, porquanto tal fenômeno ocorre quando dois entes federados exigem o pagamento de tributo por um mesmo fato gerador, o que não ocorre na espécie.

• Dessa forma, ressalvada a subsistência da destinação rural do imóvel (Tema 174 do STJ), estando preenchidas as condições elencadas no art. 32 do Código Tributário Nacional - CTN, é de se considerar válidos o lançamento e a cobrança do IPTU sobre os imóveis que a lei municipal passou a definir como pertencentes à zona urbana da cidade.

Desde a data do registro do loteamento, considera-se do domínio do Município as vias e praças, os espaços livres e as áreas destinadas a edifícios públicos e outros equipamentos urbanos.

1 (um) milhão de metros quadrados.

• Observe que Estados e União jamais aprova loteamento e desmembramento.

• A União, para além de jamais aprovar loteamento e desmembramento, também não tem competência para disciplinar a aprovação dos municípios. Apenas os Estados foram incumbidos dessa função.

• A única exceção prevista é a exigência de exame e anuência prévia da autoridade metropolitana, nas hipóteses do parágrafo único, sem prejuízo da competência final do Município para aprovar o projeto. Ou seja, ainda assim, é o município quem aprova o projeto, a despeito da anuência da autoridade metropolitana.

• No mais, os incisos desse artigo, reitere-se, não determinada a aprovação ao Estado, mas sim determina que ele discipline a aprovação do município nestas peculiares hipóteses.

As diretrizes expedidas durarão, no máximo, 4 anos.

Obs.: Não existe prazo administrativo nesse lei de 2 anos!

Apresentação de projeto contendo:

• a) Desenhos técnicos;

• b) Memorial descritivo;

• c) Cronograma de execução das obras, com duração máxima de 4 (quatro) anos;

• d) Certidão atualizada da matrícula da gleba, expedida pelo Cartório de Registro de Imóveis competente;

• e) Certidão negativa de tributos municipais;

• f) Instrumento de garantia das obras, conforme exigência do art. 18, §4º (ex: caução, seguro, fiança bancária etc.);

Apresentação perante a Prefeitura Municipal ou ao Distrito Federal, quando for o caso.

Art. 18. Aprovado o projeto de loteamento ou de desmembramento, o loteador deverá submetê-lo ao registro imobiliário dentro de 180 (cento e oitenta) dias, sob pena de caducidade da aprovação, acompanhado dos seguintes documentos:

• § 4º O título de propriedade será dispensado quando se tratar de parcelamento popular, destinado às classes de menor renda, em imóvel declarado de utilidade pública, com processo de desapropriação judicial em curso e imissão provisória na posse, desde que promovido pela União, Estados, Distrito Federal, Municípios ou suas entidades delegadas, autorizadas por lei a implantar projetos de habitação.

Embora essenciais as diretrizes dos arts. 6º e 7º, acaso se trate de município com menos de 50 mil habitantes e o plano diretor preveja diretrizes de urbanização para a área do parcelamento; lei municipal pode dispensar tais diretrizes da Lei 6.766.

Na inércia do loteador, o Município poderá solicitar por iniciativa própria o registro das áreas destinadas ao uso público.

No contexto da Lei do Parcelamento do Solo, o silêncio da Administração, quanto á aprovação ou não das obras, gerará a recusa tácita. Portanto, nesse caso:

• Silêncio administrativo = REJEIÇÃO

I work pretty

so in short, Flat Hierarchies are best for quick access surface information, while deep hierarchies are best for more well documented and organied information.

Deeper hierarchy give the illusion of simplicity while actually being more complex whereas a flat hierarchy may seem crowded but ultimately it is easier to navigate through. personally i think flat hierarchy should be the standard with deep hierarchy being reserved only for websites where the depth is inevitable. but ultimately it depends on your audience. if your audience is specialized and knowledgeable on your website (one example would be adobe users) a deep hierarchy would be better for such users) however if your audience is a more general broader audience (example would be people using a government website) its best to keep your hierarchies relatively flat and easy to navigate.

The first takeaway is that a flat hierarchy has a lot of options that are visible to the user but with fewer items to the click on below. The second takeaway is a deep hierarchy has fewer options but more subcategories underneath the main options The third takeaway it is important to understand your users to help determine what sort of hierarchy to use.

The drawback to flat hierarchies is that the amount of options for the top level navigation can be overcrowded and it can overwhelm users. Meanwhile, using deep hierarchies can create vague top level categories that may confuse users. Going too far either way will create an unpleasant user experience, so it is better to have broader structures (more options) so that users know what to expect and can find what they need sooner.

I have never really thought of it I after diving deeper into Flat and Deep Hierarchies, I think I prefer a Deep Hierarchy because it appears more simple at the top levels that is easier to navigate, and would be easier to find different subcategories for what you are looking for.

I think I've see more deep hierarchies more than flat hierarchies. Flat hierarchies tend to excel when you have a very simple website whereas the deep ones have more information in each categories. I wonder what a hybrid would look like?

While the Flat Hierarchie is good for reducing the amount of clicks that a user must execute when navigating to their content, it's drawback is that it can also frusturate the user if there is an overload of content that they must search through. This means that their is no perfect soulution and that developers must understand what their users are doing on the site, and use the best architecture that fits their user's needs. Also, the developer can use a hybrid approach that will use both architectures to best suit the user's needs.

Flat hierarchies are easier to see and interact with which is good and simple to use. Deep hierarchie has a few which is good because it appears more simple at the top level. I like where its simple to use and that there isnt alot od flat like a gov website.

As a user, I definitely prefer a flat hierarchy because it's much easier to navigate. I like being able to find what I'm looking for with just a few clicks instead of digging through multiple layers of menus and subcategories. It saves time and feels less confusing, and makes the website more user friendly

Flat Hierarchy makes options more visible to users and users can see categories right away. However, this could overwhelm users. Deep hierarchy appears more simple at the top level, but the full structure is not visible right away. Finding the right balance is key to designing an effective website.

I think a combination of these two hierarchies is the optimal way to format a website. If you go too far one way or the other it can make the website feel too repetitive or bloated in comparison to mixing the two together in a meaningful way.

One thing I found interesting from the video was how user behavior can be very different from what designers expect. It shows why testing and getting feedback from real users is so important. Even a design that seems obvious to the creator can be confusing to someone using it for the first time.

My takeaway from the Flat Hierarchy is that eveyrthing is right infont of you you can see eveyrthing, but at the same time many of the users wont pay attention to eveyrthing and in this situation the user wont get through all of the information so the user will miss out on the information that is provided vs Deep Hierarchies there are categories but its not many of them with limited information in there that everyone can go through it and understand what is going on. Aslo there are subcatergories inside a catergorie this will give you topics to click and it will give you a description to what you are going to learn and read. Overall, both of these topics are very interseting and how they work to present the content.

I found it interesting that neither flat nor deep hierarchies are automatically better the best structure depends on how users think about and search for information. A good idea is to test navigation with real users early because what seems organized to designers may not match users mental models at all.

In my opinion, this video makes it seem like deep hierarchies are not a good thing. In the flat, where she lists several pros and very few cons, she immediately goes through a large list of cons and only one pro in deep hierarchy. I feel like deep hierarchies are not as intuitive because when I go to a certain website I just want to find what I need and log off, not spend a large amount of time digging to find what I need.

I found it interesting that neither flat nor deep hierarchies are automatically better the best structure depends on how users think about and search for information. A good idea is to test navigation with real users early because what seems organized to designers may not match users mental models at all.

I think that having a cluttered top-level hierarchy is a big issue with a lot of websites. Honestly, I feel that Amazon falls into this category big time. while I'm aware that Amazon is a big website, their navbar has a wide variety of options, while useful, clutter the top of the screen on every page. for example: "health AI" and "groceries" don't need to be at the top of the screen.

Flat hierarchies offer high discoverability and lower interaction costs by putting more options upfront, but they risk overwhelming users if the top tier becomes too crowded. Deep hierarchies keep the initial view simple with fewer categories, but they often lead to vague naming, hidden content, and higher cognitive strain as users dig through multiple sub-levels.

Delete Test

New updated tag

Case#: The patient is male, 3 years and 10 months old. Ethnicity not specified

DiseaseAssertion: The patient is asserted to have CTLA-4 insufficiency.

FamilyInfo: Patient's mother has type 1 diabetes and autoimmune thyroiditis. The patient's maternal aunt has celiac disease and Lenox-Gastaut syndrome.

CasePresentingHPOs: HP:0001873 (Thombocytopenia), HP:0011947 (Respiratory tract infection), HP:0000988 (skin rash), HP:0000967 (Petechiae), HP:0034752 (Axillary lymphadenopathy), HP:0001047 (Atopic dermatitis), HP:0001903 (Anemia), HP:0012234 (Agranulocytosis),

CaseHPOFreeText: Patient had a mild upper respiratory tract infect followed by a small-spotted hemorrhagic rash and skin bruising. Physical examination was significant for punctate petechiae on skin and soft palate, as well as enlarged axillary lymph nodes bilaterally. On a subsequent visit patient was

CaseNotHPOs:

CaseNotHPOFreeText: Bone marrow biopsy did not reveal any abnormalities.

CasePreviousTesting:

GenotypingMethod:

PreviouslyPublished No prior article is known to contain information on the same proband.

Variant:

ClinVar:

gnomAD:

SupplementalData:

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Australia’s mental health system. https://www.aihw.gov.au/mental-health/overview/australias-mental-health-system Bartholomaeus, J. D., Collier, L. R., Lang, C., Cations, M., Kellie, A. R., Inacio, M. C., & Caughey, G. E. (2023). Trends in mental health service utilisation by Australia’s older population. Australasian Journal on Ageing, 42(1), 159–164. https://doi.org/10.1111/ajag.13118 Bruzzo-Gallardo, S., Genie, M. G., Gallagher, R., & Paolucci, F. (2025). Telehealth evolution and policy response in Australia: Insights from the COVID-19 pandemic. In P. Ordóñez De Pablos, M. N. Almunawar, & M. Anshari (Eds.), Information technologies in healthcare industry (Vol. 5, pp. 561–592). Academic Press. https://doi.org/10.1016/B978-0-443-30168-1.00002-5 Carswell, O., Morgan, L., Wait, S., Ruszanov, A., & Valiotis, G. (2023). Health system readiness for innovation: Putting research into practice to drive effective implementation. The Health Policy Partnership. https://www.healthpolicypartnership.com/app/uploads/Health-system-readiness-for-innovation-putting-research-into-practice-to-drive-effective-implementation.pdf Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 De Guzman, K. R., Snoswell, C. L., Caffery, L. J., Wallis, K. A., & Smith, A. C. (2022). Costs to the Medicare Benefits Schedule for general practitioner consultations: A time-series analysis. Journal of Telemedicine and Telecare, 28(10), 726–732. https://doi.org/10.1177/1357633X221122135 De Guzman, K. R., Snoswell, C. L., & Smith, A. C. (2022). The impact of telehealth policy changes on general practitioner consultation activity in Australia: A time-series analysis. Australian Health Review, 46(5), 605–612. https://doi.org/10.1071/AH22058 Garavand, A., Mohseni, M., Asadi, H., Etemadi, M., Moradi-Joo, M., & Moosavi, A. (2016). Factors influencing the adoption of health information technologies: A systematic review. Electronic Physician, 8(8), 2713–2718. https://doi.org/10.19082/2713 Gayathri, S., & Buvaneswari, P. S. (2019). The Technology Acceptance Model: A review of theories and models. https://ijrar.org/papers/IJRAR19K3028.pdf Górczak, K., Burzykowski, T., & Claesen, J. (2025). A hierarchical negative-binomial model for analysis of correlated sequencing data: Practical implementations. Bioinformatics Advances, 5(1), vbaf126. https://doi.org/10.1093/bioadv/vbaf126 Hall Dykgraaf, S., Desborough, J., Sturgiss, E., et al. (2022). Older people, the digital divide and use of telehealth during the COVID 19 pandemic. Australian Journal of General Practice, 51(8).* https://www1.racgp.org.au/getattachment/9863b056-b31f-4f0d-bc06-053cec8f6b5b/Older-people-and-use-of-telehealth.aspx Hashmi, R., Alam, K., Gow, J., Alam, K., & March, S. (2023). Inequity in psychiatric healthcare use in Australia. Social Psychiatry and Psychiatric Epidemiology, 58(4), 605–616. https://doi.org/10.1007/s00127-022-02310-1 Jayawardana, D., & Gannon, B. (2021). Use of telehealth mental health services during the COVID 19 pandemic. Australian Health Review, 45(4), 442–446. https://doi.org/10.1071/AH20325 Klaic, M., Kapp, S., Hudson, P., Chapman, W., Denehy, L., Story, D., & Francis, J. J. (2022). Implementability of healthcare interventions: an overview of reviews and development of a conceptual framework. Implementation science: IS, 17(1), 10. https://doi.org/10.1186/s13012-021-01171-7 Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding psychosocial barriers to healthcare technology adoption: A review of TAM technology acceptance model and unified theory of acceptance and use of technology and UTAUT frameworks. Healthcare, 13(3), 250. https://doi.org/10.3390/healthcare13030250 Lee, J. S., Bhatt, A., Pollack, L. M., Jackson, S. L., Chang, J. E., Tong, X., & Luo, F. (2024). Telehealth use during the early COVID 19 public health emergency and subsequent health care costs and utilization. Health Affairs Scholar, 2(1), qxae001. https://doi.org/10.1093/haschl/qxae001 Le, J. T., Mahoney, A. E. J., Court, J. L., & Shiner, C. T. (2025). Barriers and facilitators of digital mental health use in regional, rural, and remote Australia. Australian Journal of Rural Health. https://doi.org/10.1111/ajr.70011 Mbatha, B. (2024). Diffusion of innovations: How adoption of new technology spreads in society. In Information, knowledge, and technology for teaching and research in Africa (pp. 1–18). Springer. https://doi.org/10.1007/978-3-031-60267-2_1 MBS Review Advisory Committee. (2024). Telehealth post-implementation review: Final report. Australian Government Department of Health. https://www.health.gov.au/sites/default/files/2024-06/mbs-review-advisory-committee-telehealth-post-implementation-review-final-report.pdf McMaster, K. (2025). Australia’s shift to digital health—How telehealth is redefining mental health care. https://healthhub.hif.com.au/mental-health/australia-s-shift-to-digital-health-how-telehealth-is-redefining-mental-health-care RACGP. (2024). The RACGP position on the use of telehealth in general practice. https://www.racgp.org.au/advocacy/position-statements/view-all-position-statements/clinical-and-practice-management/racgp-position-telehealth-general-practice Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. https://books.google.com/books?id=9U1K5LjUOwEC Services Australia. (n.d.). Statistics—Item reports. http://medicarestatistics.humanservices.gov.au/statistics/mbs_item.jsp Wang, C. P., Mkuu, R., Andreadis, K., Muellers, K. A., Ancker, J. S., Horowitz, C., Kaushal, R., & Lin, J. J. (2024). Examining and addressing telemedicine disparities through the lens of the social determinants of health: A qualitative study of patient and provider during the COVID 19 pandemic. AMIA Annual Symposium Proceedings, 2023, 1287–1296. https://pmc.ncbi.nlm.nih.gov/articles/PMC10785927/ World Health Organization. (2022). Health systems resilience toolkit. https://www.who.int/publications/i/item/9789240048751

Overall, substantial telehealth use was associated with COVID 19 telehealth expansion. However, this was followed by a reversion to a stable low-level range. Adjustment for state, age, and gender, with the ITS augmented Negative Binomial GEE model, ensured a better capture of the national pattern. Significant differences persisted across states/territories, age groups, and gender throughout the study period. Higher telehealth share was noted in VIC, SA, younger and mid-adult age groups, as well as among females.

Discussion Principal findings The study was conducted to understand the evolution of telehealth for selected mental health services in Australia using national, population‐level Medicare data disaggregated by state/territory, age, and gender. The study indicates that there was a sudden rise in telehealth for GP mental health plan reviews due to COVID 19 expansion in early 2020, followed by a decline and a stabilisation at a lower level through the study period. At the national level, telehealth accounted for 0.5% of services at baseline (2020 Q1), peaked at almost 30% in 2020 Q2, before settling at about 13% by 2025 Q3. The ITS augmented negative binomial GEE models captured the abrupt step change and subsequent attenuation better than time-only or composition-adjusted models, with incremental gains from seasonal adjustment. Persistent differences were evident by state (higher in VIC and SA; lower in WA and QLD), age (higher in 0–4 and mid adult groups; lower in the oldest age groups), and gender (slightly higher in females). A large positive ITS step effect observed in 2020 Q2 was due to the immediate and unrestricted availability of universal telehealth funding and the shift to remote health services on account of pandemic-related restrictions, more of a substitution of in-person services. The negative post-interruption slope seen is suggestive of a general return of in-person services. The stability achieved through the study period in telehealth share, as indicated by 2025 Q3, suggests that the integration of telehealth has become a permanent feature of mental health services in Australia. Differences in telehealth share in geography and demography may reflect the disparities in digital access and literacy, provider supply, and patient mix (Wang et al., 2024; Hall Dykgraaf et al., 2022). The stark disparities between VIC and SA versus WA and QLD in telehealth share may map onto differences in urbanicity, practice models, and historical telehealth infrastructure. Higher shares in telehealth use among young children and mid adults, and lower use in the oldest groups, may be related to caregiver-mediated access (for young children), and related to employment among mid adults. Comparison With Previous Literature The findings of this study extend the previous studies on telehealth use in the initial pandemic-era (Jayawardana & Gannon, 2021; RACGP, 2024), revealing long-term stability after the early surge (Lee et al., 2024). This study also adds to the literature the quantification of population mean rates with nested NB GEE and ITS specifications. While previous studies reported rapid uptake of telehealth in the immediate pandemic period (Jayawardana & Gannon, 2021; RACGP, 2024), we have shown by a 23-quarter perspective analysis the sustained, moderate telehealth use over time, with clear geographic and demographic disparities and questions on equity. Strengths and contributions Leveraging on comprehensive national administrative datasets, this study was able to prepare a growth curve GLM, characterising the telehealth long-run adoption arc, and estimated population mean telehealth rates using NB GEE models. Furthermore, the study reinforced the robustness of the nested model through comparison and in-sample error metrics (MAE/RMSE). Limitations The limitations associated with this study include the issues involved in the use of administrative datasets. Hence, the study could not capture clinical appropriateness, symptom severity, socioeconomic circumstance, or patient-level outcomes. Furthermore, the ITS approach assumes a common interruption point (2020 Q2) and a linear post-interruption trend on the scaled time index, introducing unobserved heterogeneity. Residual confounding from unmeasured factors (e.g., the digital capability of a general practice) may have limited the findings of this study. Finally, the study focused only on GP mental health plan review items. Hence, the study findings may not be generalisable to other mental health services. Policy and practice implications The stable midrange telehealth share suggests that telehealth has become a normal part of routine GP mental health care, rather than a transient substitute. Policymakers need to continue to invest in stable reimbursement, reliability of platforms, and improved practice workflow, with a view to mitigating geographic and age-related disparities. It is also imperative to embed telehealth monitoring into mental health services monitoring and evaluation, towards improving hybrid mental health care models and reducing inequities. Future research Future studies should consider the linkage of item data to patient-level sociodemographic indicators in the analysis of equity-focused data, highlighting who benefits more from sustained telehealth use. Researchers should also explore whether the choice of modality affects care continuity, referrals, and outcomes. Conclusions Telehealth use for the review of mental health plans by GPs in Australia experienced a sharp pandemic-era expansion. This was followed by a stable, durable telehealth share. An ITS augmented NB GEE framework best captured the national trajectory and shed more light on enduring differences by state/territory, age, and gender. These findings support telehealth’s ongoing role within hybrid primary mental health care and highlight the need for policies that sustain access while addressing uneven adoption across populations and geographic locations.

share had settled at 12.7%.

Figure 2: National mental health GP consultations, by modality

Geographic variation Across states/territories, telehealth share ranged from 8.2% in WA to 17% in VIC. There was a general rise in telehealth in early 2020, followed by stabilisation, which differed in magnitude and persistence. VIC and SA had persistently higher telehealth share. On the other hand, WA and QLD (10.8%) had comparably lower telehealth shares throughout the study period. The other states/territories had telehealth share with the range: NSW (12.5%), TAS (11.9%), ACT (11.8%), and NT (11.4%).

Table 3: Key characteristics of in-person and telehealth equivalents (national and subgroups) Characteristic Value Study period (quarters) 2020Q1–2025Q3 (N = 23 quarters) National total services per quarter Range: 133,415–208,958 services per 100,000 population National telehealth services per quarter Range: 955–55,901 services per 100,000 population Telehealth share (national) Mean 14.5%; median 13.1% (share = (phone+video)/(in-person+phone+video)) Baseline telehealth share (2020Q1) 0.526% (955 / 181,507) Peak telehealth share (quarter) 29.52% in 2020Q2 (55,901 / 189,357) Latest quarter (national) 2025Q3: share 12.67% (18,229 / 143,907) Latest by gender – Female 13.35% (observed share, national) in 2025Q3 Latest by gender – Male 11.41% (observed share, national) in 2025Q3 Latest by age – Highest share 0–4 years: 18.53% (observed share) in 2025Q3 Latest by age – Lowest share 75–84 years: 7.83% (observed share) in 2025Q3 Latest by state – Highest share VIC: 17% (observed share) in 2025Q3 Latest by state – Lowest share WA: 8.2% (observed share) in 2025Q3

Age Group Patterns In the most recent period (2025 Q3), the highest telehealth share occurred in people aged 0-4 years and 35-44 years. Older groups (75-84 years) as well as ≥85 years had a telehealth share of 7.8 and 7.9% respectively. There were sustained differentials in telehealth share across the study period. Gender Differences There were slight differences in the telehealth share by gender. In the latest quarter, the observed telehealth share was 13.3% in females and 11.4% in males, compared to 13.4% (females) and 11.4% (males) predicted by the NB GEE model. Model Effects (GEE / ITS) Model M1–M4 comparison results (Appendix 1) showed that the ITS augmented model (M3) indicated a markedly improved fit (MAE 0.0203; RMSE 0.0275) compared with the time-only and demographic-adjusted models (M0–M2). Model fit appears to improve with adjustment for seasonality in M4 (MAE 0.0200; RMSE 0.0272). The ITS step term was positive (β = 4.09, 4.17), suggesting an immediate change in both M3 and M4 models. The slope term following the interruption was negative, indicating a gradual decline of the early surge.<br /> The models developed here reveal a limited fit using time only spline (M0), an improved fit with demographic adjustment (M1), a minimal additional gain from time interactions (M2), and a significant improvement with ITS (M3, M4). The models incorporating ITS aligned more closely with observed values (Figure 3). Table 4: Model coefficients Model Term Coef_logRR M3 ITS_step 4.09 M3 ITS_slope_scaled -2.38 M4 ITS_step 4.17 M4 ITS_slope_scaled -1.02

Figure 3: Telehealth share trends

Results Study Population and Descriptive Characteristics Study population and overall service volumes Across the study period (2020Q1–2025Q3), the panel dataset comprised state × gender × age × quarter service counts (per 100,000 population) with 3,680 valid strata, excluding cells with zero total mental health services. National quarterly total mental health services ranged from 134,866 to 208,958 services per 100,000 population, and quarterly telehealth counts ranged from 955 to 55,901 per 100,000 (Table 1). Overall trends in telehealth use At baseline (2020 Q1), the proportion of telehealth use was 0.5 % of all mental health service use. There was a marked change in 2020 Q2, where telehealth use rose to 29.5% (Figure 2). From mid 2020 onward, a steady decline in the telehealth shares was observed until it became stable. By the end of the study period (2025Q3), the national telehealth share had settled at 12.7%.

Diagnostics and sensitivity We conducted diagnostics and sensitivity tests for the model using observed versus fitted national shares for the GLM and GEE models, respectively, assessing trend capture and smoothing behaviour. We used Python (pandas, patsy, statsmodels, matplotlib) for data manipulation and modelling.<br /> Ethical Considerations This study was part of a larger study for which ethical clearance has been obtained (ETH2023-0357) from the UniSQ Human Research Ethics Committees (HREC), Toowoomba. We used publicly available data and de-identified datasets. Hence, informed consent was not applicable in the study.

Excess telehealth (Excessq) indicates additional telehealth services in quarter 𝑞 compared with what was expected to have occurred if the baseline 2020Q1 telehealth share had persisted. This was defined as, Excessq = Tq − E (Tq), where Tq is the observed number of telehealth services (phone + video) in quarter 𝑞, E (Tq), the expected telehealth activity in quarter 𝑞 based on the baseline share. We excluded strata with zero total services, preventing undefined proportions. Covariates Age group: This was captured in intervals (0-4, 5-14, 15-24, 25-25-34, 35-44, 45-54, 55-64, 65-74, 75-84, >=85). Gender: This was categorised as male and female. State/territory: This includes the Australian Capital Territory (ACT), New South Wales (NSW), Northern Territory (NT), Queensland (QLD), South Australia (SA), Tasmania (TAS), Victoria (VIC), and Western Australia (WA). Time: The quarterly time used in this study was encoded using an integer index representing sequential quarters since 2000 (e.g., 2020Q1 → 80). This was then normalised to the unit interval to improve numerical stability in spline estimation. This scaled time index (t_scaled) was used in all model specifications. Statistical Modelling Growth Curve Model Using aggregated per-quarter national totals, the study derived the national telehealth total as well as the national total mental health service as described above. Estimating the temporal trajectory of telehealth adoption at the national level, we fitted a binomial GLM with a logit link to stratum-level (gender × age × quarter) telehealth proportions. In this, we used outcome (telehealth proportion for each stratum), weights (stratum total service counts, i.e, frequency weights), predictor (flexible smooth function of time using a B spline basis with 5 degrees of freedom), and covariance (HC1 robust sandwich estimator for inference on spline coefficients), accounting for heteroskedasticity from differing stratum sizes. Generating the spline design matrix and model fit, we used patsy and statsmodels. The study computed the predicted telehealth shares and 95% confidence intervals for each quarter using the fitted GLM.

Negative Binomial Generalised Estimating Equations (GEE) Core specification Data was reshaped into one record per state × gender × age group × quarter, summing modalities as above to construct telehealth total, total mental health services, and telehealth share, underpinning the GEE and all nested model variants (M0–M4). Setting all negative values in the dataset to zero, we removed rows where the total mental health services (telehealth + in-person) were equal to zero. The study complemented the share model with a rate-based analysis on counts, estimating population mean telehealth rates using GEE with Negative Binomial mean–variance structure and log link, at the state × gender × age panel level: i) Outcome (telehealth counts 𝑌𝑖, 𝑞); ii) Offset (log (𝑁𝑖,𝑞) - modelling the rate per total mental health services); iii) Time (B spline, df=5 in scaled quarter); iv) Covariates (state, gender, age group - categorical main effects); v) Working correlation [AR(1) within each panel 𝑖 across quarters]; and vi) Inference [empirical (sandwich) covariance for robust standard errors]. Nested multivariable models & ITS To explain trends and quantify the interruption (assumed at 2020 Quarter 2), the study estimated a set of nested models: M0 (time only): Yi,q ∼ spline(time); M1 (+ composition): M0 + State + Gender + Age (main effects); M2 (+ differential slopes): M1 + Gender×time + Age×time (linear time interactions); M3 (ITS): M2 + ITS step (post 2020Q2) + ITS slope (quarters since 2020Q2; scaled); and M4 (ITS + seasonality): M3 + quarter of year fixed effects (C(qnum)). For each model, we computed national predictions. A consolidated model comparison table (MAE and RMSE on the national share scale) was also computed. In-sample predictive error using MAE and RMSE on the national share scale was summarised.

Variables Outcome variables, including measures derived The study defined telehealth total as the sum of the MBS telephone item (92127) and videoconference (91115), while total mental health services represent the sum of in person (2713) and telehealth total. For each statexgender×age×quarter stratum, we computed telehealth total (telephone + videoconference) and total mental health services (telehealth total + in person) Telehealth share: This is the proportion of total mental health services that were offered via telehealth, expressed in percentages (telehealth total/total mental health services X 100). This was computed for each quarter. Synthetic Baseline Telehealth Activity: This was defined as the estimated “excess” telehealth adopted relative to the patterns observed before the pandemic. Applying a fixed baseline telehealth share observed at Quarter 1, 2020, we computed the expected telehealth activity under the baseline for each quarter as follows: E (Tq) = p2020Q1 * total mental health servicesq, where E (Tq) is the expected number of telehealth services in quarter 𝑞 under the fixed baseline telehealth share, p2020Q1 is the baseline telehealth share estimated from 2020Q1 estimated in (i) above.

Study Period Nationwide expansion of telehealth services occurred in March 2020 in response to the COVID-19 pandemic, its effects, and related containment measures (Bruzzo-Gallardo et al., 2025), marking the introduction of unrestricted telehealth services in Australia. Furthermore, GP mental health consultation items 2713, 92115, 92127 were replaced by time tiered general attendance items from November 2025. Hence, the period of the study was limited to Quarter 1, 2020, to Quarter 3, 2025. The primary aims of the analyses in this study include: (i) Describing the national evolution of telehealth share over time; and (ii) Estimating population mean telehealth rates, adjusting for state/territory, age group, and gender. The study included quantification of the impact of a policy interruption (COVID-19 pandemic-related) using an Interrupted Time Series (ITS) specification, embedding it in a Negative Binomial GEE.

Methods Study setting Source of data The study analysed publicly available data on GP mental health consultations using national, administrative datasets from the Medicare Benefits Schedule (MBS), capturing all Medicare claims of outpatient mental health services in Australia. The dataset includes information on mental health consultations provided by GPs. The aggregated datasets include reported month of service use, age group, and gender by State/Territory. The national totals of the services were also included. The MBS item codes indicate the type of service, its duration, and the modality of service offered (in-person, videoconference, and telephone). Datasets used for the study were obtained from the website of Services Australia (Services Australia - Statistics - Item Reports, n.d.). These included monthly counts of services per 100,000 population. The three modalities of GP mental health consultation of at least 20-minute duration were examined: In-person, telephone, and videoconferencing, represented respectively by MBS items 2713, 92115, and 92127. These are defined as follows: i) 2713 - This refers to In-person consultation by a GP for a mental condition; ii) 92115- This indicates videoconference consultation by a GP for mental health condition; and iii) 92127 - This represents telephone consultation by a GP for mental disorders. These MBS items were used for mental health services, including relevant history taking, treatment, and advice, referrals for additional services or treatments where indicated, and recording the consultation's outcomes.

Quantifying the use of telehealth over a period by modality and demographic stratum requires the application of rigorous scientific methods capable of detecting patterns and changes (Bartholomaeus et al., 2023)Therefore, this study aimed to examine whether the use of telehealth services was sustained over time by applying two complementary analytical approaches: a growth curve binomial model and a hierarchical Negative Binomial Generalised Estimating Equation (NB-GEE) (Górczak et al., 2025). By combining national Medicare administrative datasets, disaggregated by age, gender, and States/Territories, this study aimed to provide novel information on telehealth use in mental health services in Australia. The findings of this study will provide policymakers with actionable evidence on the evolution, course, magnitude, and stability of mental healthcare services offered through various modalities in Australia.

Conceptual framework for the adoption and sustenance of telehealth services for mental health The conceptual framework for this study was based on the integrated evidence in the literature on the Diffusion of Innovations (Mbatha, 2024; Rogers, 2003), the Technology Acceptance Model (TAM)(Davis, 1989; Lee et al., 2025 ), the Normalization Process Theory (NPT)(Gayathri & Buvaneswari, 2019), and Health Systems Readiness Models (Carswell et al., 2023; World Health Organization, 2022). Studies have shown that new health service innovations, such as telehealth, digital tools, and other new products, services, and clinical pathways, often encounter challenges within the health system due to existing constraints and resources (MBS Review Advisory Committee, 2024). Hence, the key elements to consider in the context include the following: i) The policy environment and structures for reimbursement: The capacity of the workforce, digital literacy (in the context of telehealth), and the readiness for change in the organisational hierarchy determine the success of the new service or policy. Other factors relevant in this environment include the expectations of care and the sociocultural dimension, and the availability of infrastructure to implement the innovation. These factors determine whether the new service can be considered feasible and whether it would be implemented with speed and achieve sustainability in the long term (Klaic et al., 2022; MBS Review Advisory Committee, 2024; McMaster, 2025).<br /> ii) Adoption: The factors driving adoption include the perceived relative advantage of the new service (e.g. telehealth) in terms of convenience of use, clinical value compared with routine care (e.g. in-person consultation), opportunities to test the service before full implementation, alignment with current workflows, amongst others (Klaic et al., 2022; MBS Review Advisory Committee, 2024; McMaster, 2025; Garavand et al., 2016). iii) Implementation and integration: Successful integration depends on the extent to which the service fits into routine workflows, shared responsibilities of stakeholders, and the operational capacity of the system (Le et al., 2025). iv) Sustainment: The factors necessary for sustainment of the new service include the quality of the service, provider uptake, the satisfaction of patients who use the service, the extent to which the system is being monitored or evaluated, continuous quality improvement, organisational learning, and feedback loops (McMaster, 2025; Garavand et al., 2016) (Figure 1). The pathway a new service often follows includes adoption, implementation, sustainment, evaluation, and redesign. Continuous learning and improvement help in ensuring the refinement and sustainability of new services and products, including telehealth use in mental health services. This study is concerned with the evaluation of the use of telehealth in mental health services in Australia, where telehealth is part of the services available for mental health in the country (McMaster, 2025; Garavand et al., 2016).

Introduction Mental health is a significant public health challenge in Australia. Mental health disorders rank among the top causes of morbidity in the country. Almost half of Australians have experienced a mental health disorder at some point in their lives (Australian Institute of Health and Welfare, 2022). A previous study has also shown that the rate of use of mental health services increased significantly over time (Bartholomaeus et al., 2023). Inequities persist in access to and the use of mental health care services in Australia, especially in remote and very remote parts of the country (Hashmi et al., 2023; Australian Institute of Health and Welfare, 2025). Telehealth, designed to address barriers to mental health service use, continues to undergo significant changes with time (Australian Government Department of Health and Aged Care, n.d.). The March 2020 rapid expansion of telehealth services across Australia facilitated the uptake of mental health services offered through videoconferencing or telephone channels at an unprecedented scale. The adoption of telehealth as an alternative means of service delivery indicates an opportunity to offer services to target populations irrespective of their geographical locations. This represents a landmark shift in the organisation of mental health services nationwide. However, early adoption of health services does not guarantee the sustained use of such services. General practitioners play a key role in providing first-line mental health care services in Australia. An estimated 18 million mental health GP encounters occur annually in Australia, representing more than a tenth of all consultations by general practitioners (GPs) (Australian Institute of Health and Welfare, 2018). It was also cited that GP mental health consultation data in Medicare does not fully reflect GP mental health service provision, as GPs often provide mental health services in routine consultations (Australian Institute of Health and Welfare, 2018). Previous studies have shown the increased use of telehealth for mental health services post COVID-19 pandemic (De Guzman, Snoswell, Caffery, et al., 2022; De Guzman, Snoswell, & Smith, 2022). These studies examined the effects of telehealth on GP mental health consultations in the immediate pandemic impact period. However, there is a paucity of studies to ascertain whether telehealth was sustained in the long term.

Abstract Introduction: Telehealth expansion in 2020 in Australia led to an increase in its use in mental health services. This study was conducted to examine the evolution, magnitude, and persistence of telehealth services for mental health from 2020 Q1 to 2025 Q3. Methods. Analysing the national Medicare Benefits Schedule (MBS) administrative data, disaggregated by state/territory, gender, and age group, the study compared telehealth services with in-person consultations across 23 quarters. We used statistical methods, including a growth curve binomial model and a hierarchical Negative Binomial Generalized Estimating Equation (NB-GEE), to capture the effect of COVID-19 telehealth expansion and its sustainability, as well as disparities among different groups. Results From a baseline of 0.5% of services in 2020 Q1, telehealth use peaked at 29.5% in 2020 Q2 and stabilised at about 13% by 2025 Q3. The ITS augmented models captured the spike in telehealth and its subsequent decline. Disparities in use persisted, with higher use in Victoria and South Australia. Young children (0-4 years), middle-aged adults, and females also had comparably higher use. Conclusions Telehealth has become a stable feature of primary care mental health services. To improve equity in service delivery, continued policy support for telehealth is required.

Author Summary Why was this study done? There was a rapid expansion of telehealth services in Australia in March 2020, aimed at maintaining access to healthcare services during COVID-19. Mental health services delivered by general practitioners (GPs) were strongly affected. While telehealth use rose sharply during the pandemic, it remains unclear whether this shift was temporary or became a lasting feature of care. Understanding whether telehealth use was sustained and whether it differed by region, age, or gender is important for equitable digital health policy. What did the researchers do and find? We analysed national Medicare Benefits Schedule (MBS) administrative datasets covering all GP mental health consultations in Australia from 2020 to 2025. Telehealth use was reported to be 0.5% of total services offered in early 2020, rising to nearly 30% during the initial pandemic period, then declining and stabilising at about 13% by 2025. There was sustained telehealth use with variations across populations, States/Territories with higher uptake in Victoria and South Australia, among young children and middle-aged adults, and among females. What do these findings mean? Telehealth has become a stable component of primary mental health care in Australia. However, differences across regions and demographic groups suggest unequal access and system readiness. Policies that strengthen digital infrastructure and support equitable access are needed to ensure telehealth improves care for all populations.

Abstract Introduction: Telehealth expansion in 2020 in Australia led to an increase in its use in mental health services. This study was conducted to examine the evolution, magnitude, and persistence of telehealth services for mental health from 2020 Q1 to 2025 Q3. Methods. Analysing the national Medicare Benefits Schedule (MBS) administrative data, disaggregated by state/territory, gender, and age group, the study compared telehealth services with in-person consultations across 23 quarters. We used statistical methods, including a growth curve binomial model and a hierarchical Negative Binomial Generalized Estimating Equation (NB-GEE), to capture the effect of COVID-19 telehealth expansion and its sustainability, as well as disparities among different groups. Results From a baseline of 0.5% of services in 2020 Q1, telehealth use peaked at 29.5% in 2020 Q2 and stabilised at about 13% by 2025 Q3. The ITS augmented models captured the spike in telehealth and its subsequent decline. Disparities in use persisted, with higher use in Victoria and South Australia. Young children (0-4 years), middle-aged adults, and females also had comparably higher use. Conclusions Telehealth has become a stable feature of primary care mental health services. To improve equity in service delivery, continued policy support for telehealth is required.

Author Summary Why was this study done? There was a rapid expansion of telehealth services in Australia in March 2020, aimed at maintaining access to healthcare services during COVID-19. Mental health services delivered by general practitioners (GPs) were strongly affected. While telehealth use rose sharply during the pandemic, it remains unclear whether this shift was temporary or became a lasting feature of care. Understanding whether telehealth use was sustained and whether it differed by region, age, or gender is important for equitable digital health policy. What did the researchers do and find? We analysed national Medicare Benefits Schedule (MBS) administrative datasets covering all GP mental health consultations in Australia from 2020 to 2025. Telehealth use was reported to be 0.5% of total services offered in early 2020, rising to nearly 30% during the initial pandemic period, then declining and stabilising at about 13% by 2025. There was sustained telehealth use with variations across populations, States/Territories with higher uptake in Victoria and South Australia, among young children and middle-aged adults, and among females. What do these findings mean? Telehealth has become a stable component of primary mental health care in Australia. However, differences across regions and demographic groups suggest unequal access and system readiness. Policies that strengthen digital infrastructure and support equitable access are needed to ensure telehealth improves care for all populations.

This preprint represents an early version of this research. Following further analysis, data updates, methodological refinement, and reference verification, the study was substantially revised and expanded. A revised manuscript with updated analyses, corrected references, and revised findings was subsequently prepared and submitted elsewhere. Readers should interpret this preprint as an earlier draft rather than the final version of the research.

1. Schulz T, Long K, Kanhutu K, Bayrak I, Johnson D, Fazio T. Telehealth during the coronavirus disease 2019 pandemic: rapid expansion of telehealth outpatient use during a pandemic is possible if the programme is previously established. J Telemed Telecare. 2022;28(6):445 51. doi: https://doi.org/10.1177/1357633X20942045
2. Nalkar S, Chandak A. Telemedicine for specific populations: evaluating effectiveness and barriers in enhancing healthcare access and outcomes. J Public Health (Berl). 2025. doi: https://doi.org/10.1007/s10389-025-02625-8
3. Nicholas J, Bell IH, Thompson A, Valentine L, Simsir P, Sheppard H, et al. Implementation lessons from the transition to telehealth during COVID 19: a survey of clinicians and young peole from youth mental health services. Psychiatry Res. 2021;299:113848. doi: https://doi.org/10.1016/j.psychres.2021.113848

Appendices Appendix 1: MBS Items for Psychological Therapy Services and Focused Psychological Strategies (Mental Health and Allied Mental Health) MBS Item Description MBS Item Description In-person services Equivalent telehealth services Clinical Psychologist 80010 Professional attendance for psychological assessment and therapy lasting at least 50 minutes, provided by a clinical psychologist. The patient is referred by a medical practitioner, and the service is provided in consulting rooms. 80011 Like 80010 but provided via video conference. The patient must be in a telehealth eligible area, at least 15 kilometres from the psychologist. Psychologist 80110 Professional attendance for focussed psychological strategies services for an assessed mental disorder by a psychologist, lasting more than 50 minutes. 80111 Like 80110 but provided via video conference for patients located in telehealth eligible areas and lasts more than 50 minutes.

Appendix 2A: Trends of Costs for Clinical Psychology Services (Video), by State/Territory

Appendix 2B: Trends of Costs for Clinical Psychology Services (Video), by Age Group.

Appendix 2C: Trends of Costs for Clinical Psychology Services (Video), by Sex

Appendix 2D: Trends of Costs for Psychology Services (Video), by Age Group

       Appendix 2E: Trends of Costs for Psychology Services (Video), by State/Territory

Appendix 3: Model Summary for Vector Autoregressive Moving Average with exogenous inputs (VARMAX) model Metric Value Dependent Variables ['80011', '80111'] Observations 4608 Model VARMAX (5) + intercept Log Likelihood -78047.798 AIC 156205.596 BIC 156559.551 HQIC 156330.166

Appendix 4: Telehealth Policy Changes Relating to Mental Health and Allied Mental Health in Australia, 2017.

Appendix 5: Telehealth Policy Changes Relating to Mental Health and Allied Mental Health in Australia, 2020. Appendix 6: MBS Items for Psychological Therapy Services and Focused Psychological Strategies (Allied Mental Health), March 2020

In-person Videoconference item Telephone item (when videoconferencing facilities are not available) Duration Clinical Psychologists 80010 91167 91182 Attendance lasting at least 50 minutes Psychologists 80110 91170 91184 Attendance lasting at least 50 minutes Occupational Therapists 80135 91173 91186 Attendance lasting at least 50 minutes Social workers 80160 91176 91188 Attendance lasting at least 50 minutes

Results Cost comparison Pre-Intervention Period (November 2017 – February 2020) Before the intervention, in-person clinical psychology services showed a mean benefit paid of AU$98,199.5 (SD = 12,275) per 100,000 and a median (Q1, Q3) of AU$100,899 (93,024.5, 106,368.8) per 100,000. Psychology in-person services had a mean (SD) of AU$79,385.1 (9,670.6) and a median (Q1, Q3) of AU$81,710 (74,573.8, 86,065.8). Video consultations for MMM 4-7 had clinical psychology services mean (SD) costs of AU$169.5 (112) and psychology a mean (SD) of AU$117.9 (63.6), with respective medians of AU$128.5 and AU$112.5 (Table 1). Post-Intervention Period (March 2020 – June 2022) Post-intervention, in-person clinical psychology services showed a reduced mean benefit paid of AU$71,325.2 (SD = 14,804.7) and a median of AU$73,512.5 (Q1 = 61,343.8, Q3 = 78,740). Psychology in-person services had a mean of AU$62,901 (SD = 11,849.6) and a median of AU$65,226.5 (Q1 = 57,365.8, Q3 = 68,939). Video services for MMM 4-7 exhibited increased benefits for clinical psychology (mean = AU$1,557.6, SD = 371.8) and psychology (mean = AU$1,313.5, SD = 347.5), with higher respective medians (Table 1).

Table 1: Results of descriptive analysis of benefits paid per 100,000 for mental and allied healthcare services in Australia, Nov 2017 to Jun 2022.

Statistic Pre-Intervention (AU$) Post-Intervention (AU$) T-Statistic P value Clinical Psychology <br /> In-Person Services <br /> Mean (SD) 98199.5 (12275) 71325.2 (14804.7) 7.39 <0.001 Median (Q1, Q3) 100899 (93024.5, 106368.8) 73512.5 (61343.8, 78740) <br /> Video Services for MMM 4-7 <br /> Mean (SD) 169.5 (112) 1557.6 (371.8) -18.92 <0.001 Median (Q1, Q3) 128.5 (78.2, 272.8) 1470.5 (1334, 1821) <br /> Video Services for General Population (no geographical restrictions) <br /> Mean (SD) 25,066.8 (8,590.6) <br /> Median (Q1, Q3) 26,178 (18,060.8, 30,616.5) <br /> Phone Services for General Population (no geographical restrictions) <br /> Mean (SD) 8,212.4 (3,449.7) <br /> Median (Q1, Q3) 7,314.5 (5,923.2, 9,264.5) <br /> Psychology <br /> In-Person Services <br /> Mean (SD) 79385.1 (9670.6) 62901(11849.6) 5.7 <0.001 Median (Q1, Q3) 81710 (74573.8, 86065.8) 65226.5 (57365.8, 68939) <br /> Video Services for MMM 4-7 only <br /> Mean (SD) 117.9 (63.6) 1313.5 (347.5) -17.91 <0.001 Median (Q1, Q3) 112.5 (65.8, 158.8) 1256.5 (1105.5, 1557.5) <br /> Video Services for General Population (no geographical restrictions) <br /> Mean (SD) 16,887.4 (5,937.5) <br /> Median (Q1, Q3) 17,802 (12,945.8, 20,287.2) <br /> Phone Services for General Population (no geographical restrictions) <br /> Mean (SD) 7,173.6 (2,915.2) <br /> Median (Q1, Q3) 6,381.5 (5,326.5, 8,582.2)

Telehealth utilisation impact The utilisation of services across the three periods (March 2020 - February 2021, March 2021 - February 2022, and March 2022 - February 2023) varied significantly (Table 2). For Clinical Psychology, in-person services showed stable Utilisation across all periods, with no significant differences (F = 0.08, p = 0.927). Video services for MMM 4-7 displayed a significant increase in usage over time (F = 5.27, p = 0.010), while video services for the general population exhibited consistent Utilisation without significant changes (F = 0.35, p = 0.705). Phone services for the general population decreased significantly across the study period (F = 5.20, p = 0.011). In Psychology, in-person services demonstrated steady usage across all periods with no significant differences (F = 0.13, p = 0.879). Video services for MMM 4-7 experienced a significant rise in Utilisation (F = 8.49, p = 0.001), whereas video services for the general population remained stable (F = 0.40, p = 0.673) (Table 2, Appendices 2A-E). Table 2: Service Utilisation Trends and related costs for Clinical Psychology and Psychology Services, by Consultation Mode (March 2020– February 2023)

Service Period Mean (SD) Median (Q1, Q3) F-Statistic P-Value Mean (SD) Median (Q1, Q3) F-Statistic P-Value No. of Services Offered Per 100,000 Benefits Paid (AUD) Per 100,000 <br /> Clinical Psychology <br /> In-Person Services March 2020 - February 2021 541 (110) 566 (453, 589) 0.08 0.927 <br /> March 2021 - February 2022 525 (141) 504 (455, 640) <br /> March 2022 - February 2023 527 (69) 529 (493, 567) <br /> Video Services for MMM 4-7 March 2020 - February 2021 10 (2) 10 (10, 11) 5.27 0.01 1304.0 (276.9) 1333.5 (1224.8, 1440.8) 9.27 0.001 March 2021 - February 2022 12 (2) 12 (11, 13) 1647.8 (295.5) 1603.0 (1395.5, 1824.3) <br /> March 2022 - February 2023 13 (2) 13 (11, 14) 1787.1 (275.7) 1777.0 (1619.8, 1954.3) <br /> Psychology <br /> In-Person Services March 2020 - February 2021 665 (119) 699 (588, 721) 0.13 0.879 <br /> March 2021 - February 2022 689 (164) 677 (601, 817) <br /> March 2022 - February 2023 687 (84) 701 (650, 751) <br /> Video Services for MMM 4-7 March 2020 - February 2021 12 (3) 12 (11, 13) 8.49 0.001 1064.8 (258.8) 1084.5 (998.5, 1230.5) 11.97 <0.001 March 2021 - February 2022 15 (2) 15 (13, 16) 1411.9 (271.4) 1355.5 (1175.3, 1559.3) <br /> March 2022 - February 2023 16 (3) 16 (15, 18) 1546.5 (212.4) 1525.5 (1462.3, 1666.0)

Cost trends Interrupted time series analysis (ITSA) Interrupted time series analysis (ITSA) revealed distinct trends in healthcare costs pre- and post-intervention. An immediate reduction in total costs was observed following the intervention in March 2020 (Table 3). The Interrupted Time Series analysis for Clinical Psychology revealed an intercept of -2.83 (95% CI [-156.59, 150.92], p = 0.971), a pre-intervention trend of 12.76 per month (95% CI [2.99, 22.54], p = 0.011), and a post-intervention trend change of 16.08 per month (95% CI [2.26, 29.9], p = 0.023). For Psychology, the intercept was 16.62 (95% CI [-122.22, 155.45], p = 0.811), and trend change 20.48 (95% CI [8, 32.96], p = 0.002). The intervention increased costs for video-based services: Clinical Psychology by +29,874 (actual: 45,337; counterfactual: 15,463) and Psychology by +22,834 (actual: 38,297; counterfactual: 15,463), reflecting expanded Utilisation post-intervention (Figure 1). Residual diagnostics confirmed the model's validity, with no significant patterns suggesting autocorrelation. Seasonal adjustments accounted for cyclical variations, ensuring observed changes were attributed to the intervention.

Figure 1: ITS Analysis with Counterfactuals for Clinical Psychology and Psychology Video Services Table 3: Interrupted Time Series Model Results for Clinical Psychology and Psychology Video Services (Nov 2017-Jun 2022) Variable Coefficient (β) Standard Error t-value p-value 95% CI Clinical Psychology <br /> Intercept (Baseline Level) -2.83 76.62 -0.04 0.971 [-156.59, 150.92] Time (Trend Pre-Intervention) 12.76 4.87 2.62 0.011 [2.99, 22.54] Intervention (Immediate Effect) 363.33 219.71 1.65 0.104 [-77.56, 804.22] Time x Post-intervention 16.08 6.89 2.33 0.023 [2.26, 29.90] Psychology <br /> Intercept (Baseline Level) 16.62 69.19 0.24 0.811 [-122.22, 155.45] Time (Trend Pre-Intervention) 7.5 4.4 1.71 0.094 [-1.33, 16.32] Intervention (Immediate Effect) 135.93 198.39 0.69 0.496 [-262.17, 534.02] Time x Post-intervention 20.48 6.22 3.29 0.002 [8.00, 32.96]

Drivers of costs Data analysis showed that sex, age categories, state, and COVID-19 waves had varying costs per 100,000 for clinical psychology services. Significant differences were observed across groups: Sex (U=3688844, p<0.001), Age Category (H=2418.09, p<0.001), and other variables (Table 4). Similarly, for psychology services, there also significant differences between variables. Females had higher medians (1754.5) than males (673), with a significant U=3688844 (p<0.001). Furthermore, age categories and states exhibited significant variances (H=2241.71, H=328.92; both p<0.001) (Table 5).

Table 4: Potential sociodemographic and temporal predictors of costs of video consultations for clinical psychology services in MMM 4-7 (March 2020 – February 2023) Variable Median (Q1, Q3) Test Statistic P-Value Sex <br /> Female 1754.5 (382.8, 3256.5) 3688844 <0.001

Male 673 (0, 1232.3) <br /> Age Category <br /> 15-24 1673 (850.3, 3174.3) 2418.09

<0.001

25-34 2226.5 (1246.5, 4064.3) <br /> 35-44 1992 (1162.5, 3499.8) <br /> 45-54 1615 (890.8, 2537.5) <br /> 55-64 1072.5 (652.5, 1920.8) <br /> 65-74 530 (174, 895.3) <br /> 75-84 118 (0, 371)

=85 0 (0, 0) <br /> State <br /> ACT 737 (0, 2481.3) 81.65 <0.001 NSW 973 (394.5, 2235.5) <br /> NT 0 (0, 2677.5) <br /> QLD 969 (367, 2003.3) <br /> SA 1028 (218.8, 2033) <br /> TAS 743.5 (0.0, 1543.8) <br /> VIC 1202.5 (393.8, 2143.3) <br /> WA 998.5 (414.5, 2111.3) <br /> COVID-19 Wave <br /> First Wave 771.5 (162.5, 1661) 96.26 <0.001 Low Transmission 822.5 (104.5, 1730.3) <br /> Omicron Wave 952.5 (231.3, 2403.5) <br /> Second Wave 721.5 (0, 1632) <br /> Subsequent Waves 1182 (259.8, 2737) <br /> Third Wave 1021.5 (202.8, 2310.3)

Table 5: Potential sociodemographic and temporal predictors of costs of video consultations for psychology services in MMM 4-7 (March 2020 – February 2023) Variable Median (Q1, Q3) Test statistic P value Sex <br /> Female 1283.5 (225, 2594.3) 3688844 <0.001 Male 463 (0, 1021.3) <br /> Age Category (years) 1331.5 (690, 2780.5) <br /> 15-24 1795.5 (946.3, 3161.8) 2241.71 <0.001 25-34 1432 (890.8, 2553.3) <br /> 35-44 1182.5 (587.3, 1874.5) <br /> 45-54 818.5 (415.3, 1479.5) <br /> 55-64 395.5 (100.8, 904) <br /> 65-74 0 (0, 236.3) <br /> 75-84 0 (0, 0)

=85 <br /> State <br /> ACT 116 (0, 1023.8) 328.92 <0.001 NSW 964.5 (307.5, 1884.5) <br /> NT 507.5 (0, 1737.5) <br /> QLD 889 (288.8, 1658.3) <br /> SA 588 (158.8, 1272.8) <br /> TAS 1096 (0, 2653.3) <br /> VIC 1154.5 (450.5, 2317.8) <br /> WA 431.5 (115.3, 1015.5) <br /> COVID-19 Wave <br /> First Wave 672.5 (179.5, 1412.5) 76.65 <0.001 Low Transmission 538 (0, 1325.5) <br /> Omicron Wave 706.5 (44.3, 1555.8) <br /> Second Wave 580.5 (0, 1308) <br /> Subsequent Waves 972.5 (149.8, 2131) <br /> Third Wave 732.5 (0, 1787.3)

Figure 3: Median costs of video services per 100,000 individuals, by COVID-19 wave for each state (Psychology and Clinical psychology Services, March 2020 – February 2023) Concerning COVID-19 Waves and costs per 100,000 for services, states and territories like Australia Capital Territory (ACT), Tasmania (TAS), and Victoria (VIC) exhibited the highest costs across both services, while the Northern Territory (NT) and Queensland (QLD) show moderate costs. The "Subsequent Omicron Waves" account for the largest share of costs across states, highlighting sustained telehealth demand in later pandemic waves. In contrast, the Delta Variant Wave and First Wave reflected lower initial telehealth adoption (Figure 3). Clinical psychology services consistently incurred higher per capita costs than psychology. TAS shows notably high costs for psychology. Western Australia (WA) and South Australia (SA) reported relatively lower costs (Figure 3). Vector Autoregressive Moving Average with exogenous inputs (VARMAX) model A Vector Autoregressive Moving Average model with exogenous inputs (VARMAX) employed to analyse multivariate time-series data (4608 observations) and explore the temporal and demographic effects on the costs of video consultations for clinical psychology (80011) and psychology (80111) services showed notable findings. The VARMAX (5) model with an intercept term captured dynamic interactions over time. For clinical psychology services, the baseline cost was $1,398.14 (p < 0.001). Significant lagged effects indicated cost persistence. Costs declined notably for individuals aged 65–74 (-$1,068.83, p < 0.001) and were lower for males (-$968.12). However, an interaction effect (65–74 × Male) showed increased costs (+$777.64, p < 0.001) (Table 6). For psychology services, the baseline cost was $1,597.82 (p < 0.001). Lagged predictors revealed persistent but diminishing effects over time. Costs decreased significantly for the ≥85 age group (-$1,564.14, p < 0.001) (Table 6). Model fit metrics (AIC: 156205.596, BIC: 156559.551, HQIC: 156330.166) and a log-likelihood of -78047.798 demonstrated strong predictive capability with balanced complexity (Appendix 3). Diagnostics showed no residual autocorrelation (Ljung-Box, p > 0.05), but heteroskedasticity was present (p < 0.001). Overall, the VARMAX (5) model effectively captured cost dynamics and demographic influences. Table 6: VARMAX Model Results for Costs of Video Consultations for Clinical Psychology Services and Psychology Services (March 2020-February 2023)

Variable Clinical Psychology Services Psychology Services Coefficient (95% CI) Coefficient (95% CI) Intercept 1398.14 (1254.65, 1541.64) 1597.82 (1494.66, 1700.98) Age Category (Ref: 15-24 years) <br /> 25-34 397.43 (272.45, 522.41) 243.13 (140.57, 345.7) 35-44 473.58 (348.2, 598.95) -377.89 (-483.03, -272.75) 45-54 -323.71 (-494.52, -152.9) -577.26 (-695.71, -458.8) 55-64 -623.48 (-813.69, -433.27) -713.7 (-831.5, -595.91) 65-74 -1068.83 (-1317.21, -820.45) -1077.52 (-1221.68, -933.35) 75-84 -1305.78 (-1858.45, -753.12) -1453.83 (-1740.37, -1167.29)

=85 -1375.8 (-2540.37, -211.24) -1564.14 (-2086.93, -1041.36) Sex (Ref: Female) <br /> Male -968.12 (-1192.69, -743.55) -1082.34 (-1244.72, -919.96) Interactions <br /> 25-34 x Male -176.03 (-465.83, 113.77) -195.97 (-416.42, 24.47) 35-44 x Male -448.88 (-774.34, -123.43) 549.47 (331.87, 767.08) 45-54 x Male 328.96 (-29.7, 687.62) 608.35 (388.97, 827.72) 55-64 x Male 472.1 (89.46, 854.75) 583 (340.27, 825.74) 65-74 x Male 777.64 (373.63, 1181.66) 814.97 (537.13, 1092.82) 75-84 x Male 1019.31 (364.45, 1674.18) 954.4 (425.77, 1483.04) =85 x Male 962.77 (-887.58, 2813.13) 1060.77 (-39.19, 2160.72)

Generalised Linear Mixed Model (GLMM) For clinical psychology, the final Generalised Linear Mixed Model (GLMM) identified significant state and COVID-19 wave effects on square root-transformed healthcare costs. The baseline cost for the reference state and baseline wave was estimated at β₀ = 34.017 (p < 0.001). State effects varied substantially, with higher costs in NSW (β = 4.472), VIC (β = 4.465), and WA (β = 4.768) compared to the reference state (p < 0.001 for all). In contrast, NT (β = -1.804, p = 0.006) and TAS (β = -2.178, p = 0.004) exhibited lower costs, while QLD and SA showed no significant differences. COVID-19 wave effects highlighted increased costs during the Omicron Wave (β = 4.222, p = 0.006), Subsequent Waves (β = 7.385, p < 0.001), and the Third Wave (β = 4.083, p = 0.003), with no significant effects during Low Transmission or the Second Wave (Table 7). For psychology services, significant COVID-19 wave effects included decreased costs during Low Transmission (β = -2.838, p = 0.019) and the Second Wave (β = -2.535, p = 0.041) but increased costs in Subsequent Waves (β = 3.967, p < 0.001). Effects during the Omicron and Third Waves were not statistically significant (Table 8). Random Effects The models demonstrated significant variability across states and sexes for clinical psychology, with state variance (σ² = 450.025) and sex variance (σ² = 250.233). Covariance between state and sex was negligible. For psychology, state variability was moderate, with random intercept variance at σ² = 34.873. Residual Analysis Residual diagnostics confirmed well-fitting models for both clinical psychology and psychology services. Residuals were symmetrically distributed around zero, with no discernible patterns against fitted values, indicating homoscedasticity. QQ-plots showed alignment with normality, with only minor tail deviations, confirming robust model performance. The results of multiple analyses in this study show that the COVID-19 pandemic's telehealth expansion significantly increased healthcare costs, particularly for clinical psychology and psychology services. Video consultations surged in usage and cost, rising from AU$169.5 to AU$1,557.6 for clinical psychology and from AU$117.9 to AU$1,313.5 for psychology post-intervention. Despite lower operational costs, telehealth's broader accessibility drove total expenditures higher. Females and younger adults (25–34) incurred higher median costs, while costs declined with age. Regional disparities showed NSW, VIC, and WA with higher costs, while NT and TAS had lower costs. Temporally, costs peaked during the Omicron and Subsequent Waves, highlighting telehealth’s role in reshaping access and healthcare spending patterns.

Table 7: Results of Generalised Linear Multilevel Effects Model- Video Services for Clinical Psychology Fixed Effect Coefficient (β) Standard Error p-value Intercept 34.017 1.105 <0.001 State <br /> ACT (Ref) <br /> NSW 4.472 0.451 <0.001 VIC 4.465 0.456 <0.001 WA 4.768 0.481 <0.001 NT -1.804 0.659 0.006 TAS -2.178 0.645 0.004 COVID 19 Wave <br /> First wave (Ref) <br /> Omicron Wave 4.222 1.312 0.006 Subsequent Waves 7.385 1.094 <0.001 Third Wave 4.083 1.153 0.003 Low Transmission -0.389 0.958 0.582 Second Wave -1.879 1.331 0.156

Random Effect Variance (σ²) State Variance 450.025 Sex Variance 250.233

Table 8: Results of Generalised Linear Multilevel Effects Model- Video Services for Psychology Effect Coefficient (β) Standard Error P-value Intercept 26.196 3.102 <0.001 Low Transmission -2.838 0.921 0.019 Second Wave -2.535 1.052 0.041 Subsequent Waves 3.967 0.854 <0.001 Omicron Wave -0.374 1.402 0.794 Third Wave 0.472 1.239 0.713 Random Effect Variance (σ²) State Variance 34.873

Discussion The telehealth policy changes introduced in Australia, particularly during the COVID-19 pandemic, were effective in maintaining healthcare access while transforming cost and utilisation patterns. Video consultations experienced high cost increases post-intervention, rising from AU$169.5 to AU$1,557.6 per 100,000 for clinical psychology and from AU$117.9 to AU$1,313.5 per 100,000 for psychology. This reflects the success of telehealth initiatives in addressing the need for alternative healthcare delivery during a period of reduced in-person accessibility. Concurrently, in-person services showed reduced costs, with clinical psychology and psychology services seeing declines of AU$26,874.3 and AU$16,484.1 per 100,000, respectively, suggesting a patient and provider shift toward telehealth models. These findings align with policy goals aimed at improving healthcare access during the pandemic. However, the increased reliance on telehealth has introduced new cost dynamics, emphasizing the need for ongoing policy evaluation and adjustment (34). One of the primary objectives of telehealth expansion was to improve access for underserved populations, particularly in rural and remote areas. The data showed a significant rise in video consultation utilisation in MMM 4-7 regions, with mean benefits for clinical psychology increasing from AU$169.5 to AU$1,557.6 per 100,000, highlighting telehealth's potential in reducing geographic barriers. However, the persistent disparities between states (e.g., higher costs in NSW, VIC, and WA compared to NT and TAS) suggest that equitable access remains incomplete, potentially due to varying levels of digital infrastructure and healthcare resource availability across regions. Demographic factors also played a role in equity. Younger adults (25–34 years) and females incurred higher costs, likely reflecting greater adoption and acceptance of telehealth among these groups. In contrast, older adults, particularly those aged 75+, demonstrated lower utilisation, which aligns with existing barriers such as digital literacy and access to technology (29). Addressing these gaps will be crucial to achieving broader equity. While telehealth reduces operational costs, such as travel and physical infrastructure, the study revealed unintended cost increases driven by higher demand. Post-intervention, total expenditures on telehealth services exceeded pre-intervention levels due to the broader accessibility and convenience of telehealth. This aligns with international findings, where expanded telehealth offerings led to increased utilisation, even for patients who might not have sought in-person care previously (30, 31). The increased demand raises concerns about the sustainability of telehealth expansion. Without appropriate measures, such as optimized service models or stricter triaging protocols, cost growth could outpace the savings derived from operational efficiencies. These findings underscore the importance of balancing access with cost control in telehealth policy design. The findings align with existing literature on telehealth’s potential to improve equity and access, particularly in rural areas. Previous studies have demonstrated that telehealth reduces barriers to care in underserved populations, including rural communities in Australia and globally. However, consistent with this study, the literature highlights challenges such as uneven adoption across demographics and regions, often tied to digital infrastructure disparities (31). The cost dynamics observed in this study are also reflected in the broader telehealth literature. While some studies note cost reductions due to efficiency gains (32), others, similar to these findings, report increases linked to higher service utilisation post-expansion (2, 12). This duality highlights telehealth’s capacity for both cost savings and demand-driven expenditure growth. Lastly, the sociodemographic patterns observed here, with greater adoption among younger and female populations, align with global trends, as these groups are generally more tech-savvy and proactive in seeking healthcare services (32). Conclusion This study showed that the introduction of telehealth improved access to mental health services in MMM 4–7 regions. However, it led to increased costs due to higher service utilisation. While video consultation costs for psychology services rose, in-person expenses declined, reflecting a shift in care delivery. In addition, cost variations were influenced by demographics, geography, and COVID-19 waves, with younger adults, females, and certain states (NSW, VIC, WA) seeing higher expenditures. It is important to consider policy adjustments to balance financial sustainability with accessibility. Future research should explore how telehealth models can be refined to enhance cost efficiency while maintaining benefits.

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Methods Study design and setting This study employed a retrospective observational design to examine the impact of telehealth expansion on the costs of mental health and allied health services in Australia. Data were collected from Medicare Benefits Schedule (MBS) claims between November 2017 and February 2023, focusing on clinical psychology and psychology services. Data sources The study used monthly consultation data obtained from Services Australia, selecting MBS items related to clinical psychology and psychology services. The study collected data for costs (Medicare benefits paid) for services lasting at least 50 minutes in rural, remote, and very remote areas (MMM 4–7 regions) (3,13-15). The list of datasets included the costs per 100,000 for the following: i) In-Person clinical psychology (80010), and psychology (80110) services; and ii) Video consultations for clinical psychology (80011) and psychology (80111) services restricted to eligible areas in rural, remote, and very remote areas (Appendix 1). Datasets for 80010, 80110, 80011, and 80111 were obtained based on the dates of introduction of the MBS items and the objectives of the study. The relevant data on services per 100,000 for the respective datasets were also obtained for the identified MBS items and period. Variables Dependent variable: Costs per 100,000 population were the dependent variable in this study. It refers to the benefits paid by Medicare on account of the delivery of mental and allied healthcare services for eligible individuals. These costs were aggregated for all consultations and described per capita. Independent variables: Independent variables in this study include: i) Rurality: This refers to the proportion of states described as rural, remote, and very remote, measured by Modified Monash Model (MMM) classification. The proportion of people located in MMM 4-7 in each state/territory was computed using population per postcode obtained from national census data and then applying the postcode-based MMM classification obtained from the Department of Health, Disability and Ageing (7). ii) Service Type: This provides a classification of the type of mental health and allied services offered, described as either psychology or clinical psychology. iii) Service Utilisation: This shows the number of service encounters within specific periods, measured in services per 100,000. iv) Demographic factors: This captures the characteristics of the population or individuals using the services and incurring the costs described in item (i) above. These include Age [in terms of groupings such as Youth (15-24 years), Young adults (25-34 years), Adults (35-64 years), and Aged (≥65 years)]. It also includes Gender, described as male/female, as provided on the website of Services Australia (16). v) States/Territory: This refers to the place (state) of domicile of the client at the time of the service provision. vi) COVID-19 Waves: This study defined COVID-19 waves in Australia as distinct periods marked by varying transmission levels, driven by different viral variants and public health measures. The waves included: First Wave (March–May 2020), involving the original strain and strict lockdowns; Second Wave (June–November 2020), which was Victoria-focused and included quarantine breaches; Low Transmission (Dec 2020–May 2021), signifying the Pre-Delta containment period; Third Wave (June–Oct 2021), including Delta outbreaks and prolonged lockdowns; Low Transmission (Nov 2021), with Post-Delta and pre-Omicron periods; Omicron Wave (Nov 2021–Feb 2022), which was marked by high cases despite vaccination; and Subsequent Waves (March 2022–Feb 2023), involving Omicron subvariant surges (16). Categorically coded, these periods enabled analysis of COVID-19's impact on costs of telehealth for mental and allied healthcare services in Australia.

Analytical Framework To investigate the implications of telehealth expansion on mental health and allied health care costs in Australia, an analytical framework comprising cost comparison, telehealth Utilisation impact analysis, cost trend analysis, and cost drivers’ analysis was conducted. Cost comparison: The study analysed datasets spanning November 2017, when video items were introduced for focused psychological interventions in rural, remote, and very remote areas (MMM 4-7), to June 2022 (15). This ensured an equivalent period: 27 months pre- and post-March 2020, when telehealth expansion occurred in Australia (14). Descriptive analyses of costs [mean (SD) and median (Q1, Q3)] were conducted for pre- and post-telehealth expansion periods, while statistical tests, including paired t-tests, independent t-tests, or ANOVA, were conducted to test the significance of differences. Telehealth utilisation impact analysis: Service utilisation trends for Clinical Psychology and Psychology were analysed across three time periods: March 2020–February 2021, marking the immediate COVID-19 pandemic period; March 2021–February 2022, representing the late COVID-19 pandemic period; and March 2022–February 2023, signifying the pandemic recovery period (3,17). Cost trend analysis: Interrupted Time Series (ITS) Analysis evaluates intervention impacts by analysing changes in time series trends before and after an event (18,19). Effective for causal inference in non-randomized settings, it is widely used in public health and policy research. ITS is therefore suitable for conducting this study to evaluate changes in the costs of mental and allied healthcare services over time following telehealth policy changes. The primary outcome variable was the costs per 100,000 individuals, expressed in three categories: i) In-Person consultation costs; ii) Video consultation costs; and iii) Total costs (sum of In-Person and Video consultations). To address skewness in the cost data, a natural logarithmic transformation was applied. For the ITS model, the intervention variable was modelled as a binary indicator, distinguishing pre- and post-March 2020 periods. Time was modelled as a continuous variable, and interaction terms were introduced to capture changes in the slope of trends following the intervention. We performed ITS analysis using ordinary least squares (OLS) regression. The model included: i) Time, to represent the overall trend before and after the intervention; ii) Intervention, to capture the immediate effect of the policy change in March 2020; and iii) Time-Post-Intervention Interaction, to estimate changes in the slope of trends following the intervention. Cost drivers’ analyses The analyses of cost drivers were conducted through multiple approaches, including Vector Autoregressive Moving Average with exogenous inputs (VARMAX) and Generalised Linear Mixed Model (GLMM). For modelling the drivers of costs, records of children aged 0 to 14 years were excluded. Access to mental health services by adults is often complicated by perceptions, stigma surrounding mental health care, workplace constraints, or financial barriers that may not apply to children (20,21). Including children in this study would introduce developmental and contextual factors that could dilute the focus, as their access to psychological services is predominantly mediated by caregivers and schools, which operate under entirely different dynamics (22). Vector Autoregressive Moving Average with exogenous inputs (VARMAX) model. The Vector Autoregressive Moving Average with Exogenous Inputs (VARMAX) model is a multivariate statistical method used to analyse and forecast time-series data. It extends the VARMA model by incorporating external variables (exogenous inputs) to improve predictive accuracy (23). VARMAX captures dynamic interdependencies between multiple time-series variables through autoregressive (VAR) and moving average (MA) components while allowing external factors to influence outcomes (24). The VARMAX model examined interactions between dependent variables and predictors with up to five lags, incorporating autoregressive terms and exogenous effects. Parameters were estimated using maximum likelihood under the assumption of normally distributed residuals. Model fit was evaluated using AIC, BIC, and HQIC, and confidence intervals were derived via the delta method. Sensitivity analyses tested alternative lag structures and interaction terms, while variance inflation factors (VIFs) checked for multicollinearity. Diagnostic tests, including residual analysis and Ljung-Box tests for autocorrelation, ensured robustness. The covariance structure accounted for near-singular matrices using a robust correction (24).

Generalised Linear Mixed Model (GLMM) A Generalised Linear Mixed Model (GLMM) extends Generalised linear models by incorporating both fixed and random effects, allowing for analysis of data with hierarchical or clustered structures. GLMMs handle non-normal response variables and model relationships using various distributions (e.g., Gaussian, Gamma) and link functions (e.g., log, identity) (25). In this study, GLMMs accounted for variability within states and demographic groups by including random intercepts for sex and state, while fixed effects captured state-level and COVID-19 wave influences on healthcare costs. This approach enabled robust analysis of complex datasets with multiple sources of variability. Data were sourced from a publicly accessible healthcare database encompassing eight Australian states and territories across multiple COVID-19 waves, including the Omicron wave, Third Wave, and other specified periods. The dataset included 4,608 observations of healthcare encounters that met the inclusion criteria of complete data on healthcare costs, state identifiers, COVID-19 wave classification, and demographic details. Observations with missing or implausible values were excluded (26). Healthcare costs, the dependent variable, were represented by items 80011 (clinical psychology) and 80111 (psychology). These were subjected to a square root transformation to stabilize variance and mitigate skewness. Independent variables included state (a categorical variable with eight levels) and COVID-19 wave classifications. Random intercepts were incorporated for sex and state to account for within-group variability (27). To ensure robust analysis, preprocessing involved addressing zero and negative values, applying square root transformations, and filtering out outliers. Bias was minimised through standardised procedures: selection bias was mitigated by including only complete records, measurement bias was addressed through uniform preprocessing, and heterogeneity bias was managed using random effects. The sample size of 4,608 observations was used based on the availability of complete and valid records. A GLMM with a Gamma distribution and a log link function was initially specified. Fixed effects for COVID-19 waves and states and random intercepts for sex and state were included in the model. Residual diagnostics evaluated normality, homoscedasticity, and outliers, leading to the exclusion of extreme residuals (greater than three standard deviations) to improve model robustness. Analyses were conducted using Python's statsmodels library for GLMM fitting and diagnostics (26-28). Ethical Considerations The study was granted ethical approval by a relevant Human Research Ethics Committee. It adhered to key ethical principles.

This study aims to investigate the implications of telehealth expansion on mental health and allied health costs in rural populations. Specifically, the study seeks to answer the following research questions: i) What were the implications of telehealth expansion on costs in rural, remote, and very remote areas? ii) What were the of costs post-telehealth expansion in rural, remote, and very remote areas? By addressing these questions, this study contributes to the growing body of evidence on telehealth's role in reshaping healthcare delivery, with a particular emphasis on equity and cost-effectiveness (11,12).

Telehealth has emerged as a transformative approach to healthcare delivery, particularly in mental health and allied health services. Its significance has grown globally, driven by its potential to overcome barriers such as geographic isolation, transportation limitations, and healthcare provider shortages (1-3). The COVID-19 pandemic further accelerated telehealth adoption, serving as a critical tool to ensure continuity of care during periods of restricted mobility and strained healthcare systems (3). Telehealth’s ability to provide virtual consultations has proven especially beneficial in mitigating access challenges during emergencies, including public health crises like COVID-19. In Australia, telehealth was rapidly expanded through significant policy interventions during the COVID-19 pandemic (4). Key among these was the introduction of Medicare Benefits Schedule (MBS) items for telehealth consultations, which enabled government-subsidized access to virtual healthcare services. These policy changes were designed to address accessibility gaps in rural and remote populations, where geographic and logistical barriers have historically limited access to in-person healthcare services (3-6). The Modified Monash Model (MMM) classification system, which identifies areas based on remoteness, underscored the critical need for telehealth in MMM 4-7 regions, where healthcare disparities are most pronounced (7). Despite the increased adoption of telehealth, there is a limited understanding of its economic implications, particularly regarding the costs incurred for mental health and allied health services. Existing studies have primarily focused on access and satisfaction outcomes, leaving gaps in knowledge about telehealth’s financial impact (8,9). Additionally, the cost dynamics among rural populations, who may benefit the most from telehealth, remain underexplored (10). Given the heterogeneity in service utilisation and demographic factors, understanding the drivers of telehealth costs is essential for evidence-based policy development.

Author Update (June 2026)

Following a subsequent audit of this preprint, some references and supporting citations were identified as inaccurate. The manuscript was later comprehensively revised, references were re-verified against original sources, and corrections were incorporated into a revised version submitted subsequently. Readers are advised to consult the annotations within this preprint regarding affected citations.

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Discussion

Key Findings This study assessed the effects of April 2020 and July 2021 bulk billing policies on the use of three selected mental health services in Australia. The analysis revealed that the April 2020 increase in bulk billing incentives was associated with a modest increase in fully subsidised in-person services (MBS 2715) as well as a slight uptick in the trend of the partly subsidised in-person services (MBS 281) without an immediate change. On the other hand, the July 2021 intervention was associated with a notable effect only on the use of fully subsidised telehealth services (MBS 92116). It was associated with a 7.7% decline in the odds of service use as well as a decline in its post-intervention trajectory. There were notable and persistent differences by gender, age group, and States/Territories across all service types, highlighting the role of structural and behavioural factors as important variables in shaping the patterns of service utilisation during widespread policy implementation in the health system.

Connection to Existing Literature Aligning with Andersen’s Behavioural Model of Health Services Use, the results of this study imply that affordability may be a key factor in service use. These findings of this study also support those of Rosenberg and Hickie (26), showing that financial incentives play a major role in service use among socioeconomically vulnerable groups. In addition, this study further buttresses the points made by O'Sullivan et al (27) that reductions in telehealth funding disproportionately harm rural patients with limited alternatives. Conversely, our results diverge from this discrepancy, which may stem from the complexity of policy implementation during COVID-19 or the lag in patient and provider response to changing billing structures. Additionally, the results of a study also indicated that patients are unlikely to revert to in-person care after adopting telehealth (28), whereas our modelled results show a statistically significant decline in telehealth uptake after the reduction in incentives, indicating that financial barriers remain influential. Hua et al(29) showed that there were gradual increases in the use of services following Medicare reforms. In the present study, the observed change in the use of services was thought to be due to several factors, ranging from the complexity of COVID-19 policy implementation to the lag in patient and provider response and the changing bulk billing structures. Additional literature also suggests continued use of telehealth after adoption (30). In contrast, our study showed a significant decline in telehealth use after the reduction in incentives, implying that financial barriers are a major factor in patients’ use of services, whether telehealth or in-person.

Interpretation of Results The link between financial incentives and service utilisation remains strong. The April 2020 bulk billing incentives likely encouraged more providers to offer fully subsidised care, particularly for MBS 2715. In contrast, the July 2021 policy change did not cause an immediate drop in raw service volumes but was associated with a 7.7% reduction in the odds of telehealth use. This suggests a dampening effect on service growth, even amid sustained demand, reflecting the sensitivity of telehealth uptake to financial settings (31,32). The findings of this study suggest that financial incentives contributed to a decline in service use. While April 2020 was associated with an immediate shift in service use counts, the modelled estimates indicate a 7.7% reduction in the odds of telehealth use. The observed contrast between the raw counts of service use and the adjusted odds highlights the significance of accounting for demographic and regional variables. This allowed the model to identify the associated policy effect from broader contextual changes. The results also suggest that the use of telehealth services may have continued in the downward trend had the billing policy not changed. This study also attests to the persistent demographic disparities in where males and older adults record lower mental health service use (33,34). In addition, differences in service use among States/Territories further reveal structural and service delivery factors, with NT residents consistently underserved. Unexpectedly, the pattern of use of the partly subsidised service (MBS 281) was relatively stable throughout the index period, possibly reflecting entrenched practice patterns among service providers or patients.

Limitations There are a few limitations of this study. First, the use of aggregated national administrative data, such as the MBS data) limits the ability to account for individual-level insights, including clinical severity or socioeconomic status, which may influence service use. Second, overlapping policy changes, including COVID-19 pandemic policies and the bulk billing reforms, may have created confounding factors that challenge causal inference. Third, our statistical models accounted for demographic and regional differences, as well as unmeasured variables such as internet access. However, the local health workforce, cultural attitudes toward mental health, and other extraneous variables not accounted for could have influenced service use.

Implications for Practice The results highlight the need for long-term, stable policy planning in mental healthcare financing. Sudden changes to health providers’ motivation in the form of incentives may influence service disruption, exacerbating inequities, in underserved communities. This study suggests that policymakers should consider the impact of financial structures on patients' behaviour and service delivery. Outreach to older adults and men, and digital literacy programs, could mitigate disparities. Continued support for telehealth infrastructure and reimbursement is especially crucial in rural areas where physical access to care remains limited (35–39). Policy implementation may be hampered by funding constraints, provider buy-in, and digital capacity. Overcoming these requires concerted efforts at all levels, including the formulation of relevant national state policies on mental health, investment in workforce development, and community-based digital inclusion efforts.

Conclusion and Future Research In summary, this study demonstrates that bulk billing policy interventions significantly influence mental health service use. In addition, it also shows that financial incentives play a key role in service use patterns. The findings highlight the need for cohesive policy frameworks that avoid abrupt reversals and better account for regional and demographic disparities. Future research should explore patient and provider perspectives on bulk billing policies and practices. Furthermore, researchers should assess the long-term effects associated with the 2023 tripling of bulk billing incentives. Addressing these gaps will support more equitable and effective mental healthcare delivery under Australia’s Medicare system.

Declaration Ethics The study was part of a larger study for which ethical clearance (ETH2023-0357) was obtained. De-identified, publicly available data was used. Hence, informed consent and participant compensation were not relevant for the study..

Hurdle Models

Partly subsidised in-person mental health service (281) The hurdle model used for the evaluation of the associated effects of the April 2020 and July 2021 interventions on service use consisted of two components, including: i) a count model, which assessed service counts where at least one service was reported used, and (ii) a zero model, which estimated the probability of any service use. Age group, gender, and States/Territories, as well as level and slope parameters, were included in the models for the April 2020 and July 2021 interventions. Before 2020, service use showed a slight upward trend (β = 0.03, 95% CI: 0.00–0.06, p = 0.028). There was no significant immediate change in service use associated with the April 2020 intervention (β = 0.65, 95% CI: -0.72 to 2.02, p = 0.355), and the trend following the intervention was positive (β = 0.05, 95% CI: 0.01–0.09, p = 0.031) (Table 2). There were no changes in service use on account of the July 2021 intervention (Immediate impact, β = -0.98, 95% CI: -12.82 to 10.86, p = 0.860); Long-term (β = 0.00, 95% CI: -0.01 to 0.01, p = 0.874) (Table 2). Compared with females, males had consistently had lower service use at both intervention points (April 2020: β = -0.36, 95% CI: -0.63 to -0.07, p = 0.015; July 2021: β = -0.63, 95% CI: -0.69 to -0.57, p < 0.001). Service use was lower among older adults, especially those aged ≥65 (April 2020: β = -2.05, 95% CI: -3.27 to -2.35, p < 0.001; July 2021: β = -2.47, 95% CI: -2.57 to -2.37, p < 0.001) (Table 2). Overall, the hurdle model results revealed that demographic and geographic disparities in service use limited the immediate effects of the interventions (Table 2). Table 2: Hurdle Model Results Showing the Impact on April 2020 and July 2021 Interventions on In-Person Mental Health Service (281) April 2020 Intervention July 2021 Intervention Logistic Regression Negative Binomial GLMM Logistic Regression Negative Binomial GLMM Variable Coefficient Std. Error P-Value Coefficient Std. Error P-Value Coefficient Std. Error P-Value Coefficient Std. Error P-Value Intercept 2.82 0.77 0.000 1.35 0.28 0.000 2.94 0.40 0.000 1.64 0.14 0.000 Time -0.01 0.02 0.540 0.01 0.01 0.513 -0.07 0.02 0.001 -0.03 0.01 0.003 Intervention 0.00 0.00 0.201 0.00 0.00 0.995 0.00 0.00 0.106 0.00 0.00 0.016 Time After Intervention 0.02 0.02 0.434 0.01 0.01 0.522 -0.01 0.03 0.747 0.02 0.01 0.244 Sex (Ref: Female) Male -1.08 0.33 0.001 -0.36 0.14 0.011 -0.79 0.17 0.000 -0.43 0.07 0.000 Age Category, in years (Ref: 15-24) 25-34 -0.33 0.83 0.690 0.13 0.23 0.577 0.50 0.44 0.251 0.10 0.12 0.426 35-44 -1.25 0.81 0.120 -0.04 0.24 0.849 -0.19 0.42 0.657 -0.27 0.12 0.027 45-54 -1.56 0.80 0.051 -0.40 0.24 0.091 -0.45 0.41 0.276 -0.64 0.12 0.000 55-64 -1.99 0.79 0.012 -0.73 0.25 0.003 -0.94 0.41 0.022 -1.15 0.13 0.000

=65 -5.58 0.79 0.000 -2.05 0.24 0.000 -4.55 0.39 0.000 -2.05 0.13 0.000 State (Ref: ACT) NSW 4.15 0.74 0.000 1.79 0.28 0.000 2.39 0.38 0.000 1.96 0.15 0.000 NT -4.83 0.78 0.000 -1.41 0.77 0.066 -3.63 0.36 0.000 -0.97 0.29 0.001 QLD 3.46 0.72 0.000 1.40 0.28 0.000 2.09 0.37 0.000 1.93 0.15 0.000 SA 1.74 0.69 0.012 0.23 0.29 0.428 1.81 0.36 0.000 0.88 0.15 0.000 TAS -1.94 0.59 0.001 -1.10 0.38 0.004 -1.50 0.33 0.000 -0.20 0.18 0.263 VIC 4.49 0.76 0.000 2.25 0.28 0.000 2.67 0.38 0.000 1.93 0.15 0.000 WA 2.33 0.69 0.001 1.33 0.29 0.000 1.49 0.36 0.000 1.18 0.15 0.000 Interactions time:State[NSW] 0.09 0.04 0.039 <br /> time:State[QLD] 0.09 0.04 0.033 <br /> time:State[SA] 0.08 0.04 0.044 <br /> time:State[TAS] 0.08 0.03 0.024 <br /> intervention:('age category')[45-54] -1.85 0.91 0.043 <br /> intervention:State[SA] -0.78 0.39 0.045   Telehealth Mental Health Service (92116) The hurdle model was also used to model the use of the fully subsidised telehealth health service (92116) and the associated effects of the July 2021 intervention. The pre-intervention trend of service use showed a small but statistically significant upward direction (β = 0.15, p < 0.001). While the intervention had no immediate effect (β = 0.00, p = 0.233), the post-intervention trend was in the upward direction (β = 0.09, p < 0.001) (Table 3). The evidence of regional disparities was clear with NSW, VIC, and QLD showing the highest service counts (VIC: logistic β = 4.49, p < 0.001; GLMM β = 3.18, p < 0.001). The lowest service use count was in NT (logistic model: β = -2.03, p = 0.006).<br /> Overall, there was no change in telehealth use immediately after the July 2021 bulk billing intervention. However, demographic and regional disparities remained significant. The AIC and BIC values indicated good model fit, and the residual diagnostics did not reveal any evidence of significant violations of model assumptions.  Table 3: Hurdle model results showing the impact of July 2021 bulk billing change on telehealth, fully subsidised mental health service (92116)

Logistic Regression

Negative Binomial GLMM

Variable Coefficient Std. Error P-Value Coefficient Std. Error P-Value Intercept 0.48 0.43 0.273 0.68 0.28 0.014 Time 0.15 0.02 0.000 0.07 0.01 0.000 Intervention 0.01 0.00 0.000 0.00 0.00 0.233 Time After Intervention -0.08 0.03 0.010 0.09 0.02 0.000 Sex (Ref: Female) <br /> Male -0.88 0.23 0.000 -0.62 0.12 0.000 Age Category, in years (Ref: 15-24) <br /> 25-34 1.20 0.54 0.025 0.33 0.19 0.080 35-44 0.08 0.49 0.876 0.10 0.20 0.612 45-54 -0.38 0.48 0.423 -0.36 0.20 0.072 55-64 -1.00 0.47 0.032 -0.78 0.21 0.000

=65 -3.70 0.45 0.000 -1.65 0.21 0.000 State (Ref: ACT) <br /> NSW 3.66 0.51 0.000 2.36 0.28 0.000 NT -1.48 0.46 0.001 -0.28 0.48 0.561 QLD 3.77 0.51 0.000 1.52 0.29 0.000 SA 2.06 0.45 0.000 0.64 0.30 0.034 TAS 0.80 0.41 0.053 0.40 0.33 0.224 VIC 4.49 0.55 0.000 3.18 0.28 0.000 WA 2.53 0.47 0.000 0.95 0.30 0.001 Interactions <br /> time:State[QLD] -0.11 0.05 0.014 <br /> time:State[SA] -0.10 0.04 0.018 <br /> time:State[TAS] -0.09 0.04 0.023 <br /> intervention:Q('age category')[>=65] -1.74 0.77 0.023 <br /> intervention:State[NT] -2.03 0.74 0.006 <br /> intervention:State[SA] 1.47 0.72 0.043 <br /> time:State[WA] -0.10 0.03 0.000 intervention:State[NT] 1.94 0.63 0.002 intervention:State[QLD] 2.18 0.40 0.000 intervention:State[SA] 2.29 0.43 0.000 intervention:State[TAS] 2.51 0.48 0.000 intervention:State[VIC] 1.44 0.40 0.000 intervention:State[WA] 2.69 0.42 0.000 time:Q('age category')[>=65] -0.04 0.02 0.028 time:State[NT] -0.13 0.04 0.001 time:State[QLD] -0.07 0.03 0.008 time:State[SA] -0.08 0.03 0.003 time:State[TAS] -0.13 0.03 0.000 time:State[VIC] -0.08 0.02 0.002

Results

Participants There were 24,626 observations in the datasets, comprising the MBS service claims from October 2018 to December 2022, stratified by age group, gender, and State/Territory of residence (NSW, VIC, QLD, SA, WA, TAS, ACT, and NT). Each MBS item had the service use count, total services provided across States/Territories. All extracted datasets had completed elements.

Trend Analysis The plot of the time series for each mental health service revealed fluctuations. The fully subsidised in-person service (MBS 2715) declined over time, while the telehealth service (MBS 92116) displayed a sharp uptick, and the partly subsidised in-person service (MBS 281) remained stable (Appendices 1 and 2).

Interrupted Time Series (ITS) with Negative Binomial, Generalised Linear Mixed Model (GLMM) Before April 2020, service use counts showed an upward trend (β = 0.03, 95% CI: 0.00 to 0.06, p = 0.028), signifying a gradual increase in the use of services. The April 2020 Intervention was not associated with an immediate change in level of service use (β = 0.65, 95% CI: -0.72 to 2.02, p = 0.355). However, there was an increase in the slope of service use β = 0.05, 95% CI: 0.01 to 0.09, p = 0.031) post intervention, suggesting service use increase continued after the implementation of the intervention. For the July 2021 intervention, there were neither significant effects on service use in the immediate (β = -0.98, 95% CI: -12.82 to 10.86, p = 0.860), nor in the post-intervention (β = 0.00, 95% CI: -0.01 to 0.01, p = 0.874) periods (Table 1, Appendix 2). The Negative Binomial Generalised Linear Mixed Models (NB GLMM) used to assess the association between States/Territories, age group, and gender, and the use of Medicare-subsidised in person service (2715) over time, showed that males had consistently lower values compared to females (April 2020: β = -0.35, p = 0.015; July 2021: β = -0.63, p < 0.001). Relative to ACT, the Northern Territory (April 2020: β = -1.44, p < 0.001; July 2021: β = -1.18, p < 0.001) had lower service counts. Older adults (≥ 65) had a slower post-April 2020 increase (β = -0.18, p < 0.001), highlighting an age-related disparity in the growth of service use (Table 1).   Table 1: Interrupted Time Series (ITS) Analysis with Negative Binomial, Generalised Linear Mixed Models for In-Person Service (2715) April 2020 Intervention

July 2021 intervention

Variable Coefficient Standard Error P-Value Estimate Standard Error P-Value Intercept 4.18 0.37 <0.001 4.94 0.06 <0.001 Time 0.03 0.01 0.028 0.00 0.00 0.041 Intervention 0.65 0.70 0.355 -0.98 5.53 0.860 Time after intervention 0.05 0.02 0.031 0.00 0.01 0.874 Sex (Ref: Female) <br /> Male -0.35 0.14 0.015 -0.63 0.03 <0.001 Age Category, in years (Ref: 15-24) <br /> 25-34 0.11 0.28 0.698 0.08 0.06 0.160 35-44 -0.10 0.28 0.714 -0.20 0.06 0.001 45-54 -0.36 0.28 0.197 -0.53 0.06 <0.001 55-64 -0.81 0.28 0.004 -0.94 0.06 <0.001

=65 -2.81 0.23 <0.001 -2.47 0.05 <0.001 State (Ref: ACT) <br /> NSW 3.09 0.28 <0.001 3.05 0.06 <0.001 NT -1.44 0.29 <0.001 -1.18 0.06 <0.001 QLD 2.71 0.28 <0.001 2.73 0.06 <0.001 SA 1.56 0.28 <0.001 1.54 0.06 <0.001 TAS 0.22 0.28 0.447 0.30 0.06 <0.001 VIC 2.95 0.28 <0.001 2.90 0.06 <0.001 WA 1.77 0.28 <0.001 1.92 0.06 <0.001 Interactions <br /> Time and age category >=65 -0.18 0.04 <0.001 <br /> Intervention and State - VIC -1.54 0.66 0.020

Statistical Analyses Descriptive Analysis Descriptive statistics were conducted, and continuous variables were reported as means (±SD) and medians (Q1, Q3). Before and after interventions, monthly service counts were computed. We also plotted scatter diagrams and lines of best fit. Time series plots, percentage changes over time, and trends across key periods were conducted. For consistent comparison, rates of service use were normalised as a percentage of each service’s peak to allow consistent comparison. Multivariate analysis The multivariate analyses conducted in this study assessed the effects associated with bulk billing reforms across age groups, genders, and States/Territories. We developed models for Interrupted Time Series (ITS) using a Generalised Linear Mixed Model (GLMM) with a Negative Binomial distribution for the fully subsidised in-person mental health service (2715). A preliminary analysis showed that the partly subsidised service (281) and the telehealth service (92116) had many zero counts. Hence, we developed hurdle models to manage excess zeros and overdispersion. We developed separate models for each service and intervention. For in-person services (2715 and 281), we assessed the effects of April 2020 and July 2021 interventions on their use, while the effects of the July 2021 intervention were examined on the use of the telehealth service (92116). We did not examine the effects of the April 2020 intervention on the telehealth mental health service, as the service was introduced the previous month. For all models, we used monthly data spanning 18 months pre- and post-intervention.<br /> Interrupted Time Series (ITS) with Negative Binomial, Generalised Linear Mixed Model (GLMM) Widely acknowledged as a suitable method for analysing quasi-experimental, longitudinal interventions, Interrupted time series (ITS) was used in this study. ( 18). The study was meant to address hierarchical and repeated measures (monthly service counts in States/Territories). Hence, integrating ITS with Generalised Linear Mixed Models (GLMMs) was appropriate, allowing for the inclusion of fixed and random effects (18,19). The preliminary data analysis indicated that overdispersion was evident in the monthly service count of in-person, fully subsidized mental health service (MBS 2715). Hence, we used the Negative Binomial (NB) distribution, which offers a better fit for count data exhibiting high variability (20–23). The study analysed both the immediate and long-term effects of the identified bulk billing interventions over time. A random residual with an autoregressive variance structure was used to account for the correlation of the count of services over time and for overdispersion. Gender, age group, and State/Territory were modelled as fixed effects, controlling demographic and geographic variables. The model was specified as follows: Log (Yit)=β0 + β1(time) + β2(intervention) +β3 (time × intervention) + β4(sex) + β5(age group) + β6(state)+ϵit with Yit representing the number of services claimed, β coefficients the model parameters, and ϵit the error term. Akaike information criteria (AIC) values and Bayesian Information Criterion (BIC) were used for assessing model fit, and residual diagnostics were evaluated.

Hurdle Models Preliminary evaluation also showed that partly subsidized, in-person service (281) and telehealth service (92116) showed zero-inflated count data pattern, indicating overdispersion and excess zeros. Studies have shown that hurdle models are used for handling zero-inflated count data, where some individuals never use a service (structural zeros), while others use it at varying rates. Being an effective method, the hurdle models were used to model these services (281 and 92116). Each model consists of a logistic regression to identify factors linked to any service use, and a Generalised Linear Mixed Model with a negative binomial distribution for non-zero counts. The study modelled age, gender, and State/Territory as independent variables., reported coefficients, standard errors, and p-values of final models and evaluated AIC, BIC, log-likelihood, and goodness-of-fit tests in adjudging model fit.   Bias Aggregated data was used in the study, ensuring uniformity across jurisdictions and services and minimising bias. The study was conducted in keeping with ethical standards (24, 25). All analyses were carried out at 5% level of significance. All analyses were conducted using Python 3.11 with the statsmodels package (version 0.14).

Study design and participants The study was conducted using publicly accessible Medicare Benefits Schedule (MBS) items datasets. The three selected mental health services assessed include: i) MBS 2715, a fully subsidised mental health in-person service for preparing a mental health treatment plan; ii) MBS 92116, a fully subsidised telehealth equivalent of MBS 2715; and iii) MBS 281, a Medicare partly subsidised in-person mental health service. The provider’s fees for each of these services were AUD 103.70. The report of this study was prepared in keeping with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. Procedures This study assessed the effects of the April 2020 increase in bulk billing incentives and the July 2021 reduction in telehealth incentives on the use of the selected mental health services. Therefore, we downloaded the monthly aggregated datasets for the relevant MBS items from the website of Services Australia. We downloaded datasets from October 2018 to December 2022 (9,10,16), to allow sufficient time for observation pre- and post-intervention, thereby ensuring temporality in the study. It is important to note that MBS item 2715 was already established before 2018, while MBS 281 was introduced in July 2018 (3,10 ).The three MBS items - 2715, 92116, and 281 were selected to explore several dimensions of change in service use. MBS 2715 and MBS 92116 (Appendix 1) relate to initiating Mental Health Treatment Plans, a reflection of help-seeking behaviour. MBS 92116, the telehealth equivalent of MBS 2715, indicates shifts in the modality of service delivery during the COVID 19 pandemic. We included MBS 281 to ensure variations in out-of-pocket expenditure as well as changes in providers' billing practices under changing bulk billing incentives. Socio-demographics The datasets for the selected MBS items were extracted, exported to Microsoft Excel documents, and prepared for analysis, including the service used by age group, gender, and date. Variables The study included explanatory variables, interventions, and an outcome variable. Explanatory variables used in the study include age (15–24, 25–34, 35–44, 45–54, 55–64, 65+), gender (male, female), and State/Territory [New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA), Tasmania (TAS), Australian Capital Territory (ACT), Northern Territory (NT)]. The two policy changes (interventions) examined include the April 2020 increase in bulk billing incentives and the July 2021 reduction in telehealth incentives. We created binary indicators, indicating pre-intervention (0) and post-intervention (5, 10, 17). The outcome variable was defined as the monthly total number of services used.

Theoretical Framework The Andersen’s Behavioural Model of Health Service Use is a widely used framework in the literature (11,12)was used to assess the use of mental health services was applied in this study. The model considers the factors that predispose to (e.g., age, gender)(13), enable (e.g., policy changes, affordability), and influence (e.g., severity of mental health conditions) the use of health services (14). This model provides a guide to the analysis of Medicare bulk-billing policy reforms and their effects on the use of mental health services in Australia (Figure 1). Mental health is a key focus of bulk billing reforms (7,8), prompting the need to assess how policies have influenced (6) pressure on the healthcare system(15), out-of-pocket expenses, and service use.

Figure 1: A Conceptual Framework illustrating the Effects of Bulk Billing Policy Changes on the Use of Mental Health Services. (Based on Andersen’s Behavioural Model of Health Services Utilisation)

Australia prides itself on being one of the few countries with a universal health system catering to the needs of all its citizens (1). Central to this system is affordability (1–3). Upticks in the cost of living have given rise to a growing concern regarding the declining bulk-billing practices by healthcare providers, and the consequent rise in out-of-pocket expenditures for healthcare services (3). Out-of-pocket expenditure, reflecting the gap between providers’ fees and Medicare rebates, continues to raise concerns for individuals and families seeking healthcare in Australia (2).<br /> With bulk billing designed to eliminate out-of-pocket costs through the Medicare Benefits Scheme (MBS), Australia aims to improve the health of its citizens (1,3). Bulk billing helps vulnerable groups like pensioners, children, and concession cardholders access healthcare. Frequent changes to bulk billing policies from 2019 to 2023 and the overlapping reforms have made evaluating bulk billing practices a challenge(1,3,4). There were several bulk billing policy reforms in Australia from 2019 to 2023, including the introduction of temporary telehealth bulk billing incentives in 2020 (1,5), and the subsequent addition of further incentives, especially for mental health care (1,3). In 2023, there was an introduction of tripling of bulk billing incentives to reverse the decline in service use among vulnerable groups (3,4). <br /> These multiple reforms were aimed at increasing access to services, improving cost effectiveness, and motivating healthcare providers. However, geographic and socioeconomic disparities persisted as well as inequities in access to healthcare due to high out-of-pocket expenditure in some locations despite the bulk billing policy reforms (1,3,4). While billions of dollars were invested to increase access to services, especially among vulnerable populations, bulk‑billing rates did not significantly improve, highlighting the complex nature of implementation and effects of system-wide policies in the health system (1,2,6). Mental health is a key health challenge in Australia, with almost 50% of Australians aged 18 years and above facing a mental health problem at least one time in their lives (7). Rising to the challenge, the Australian Government enacted policies and framework to tackle this menace (5). These include the development of the Fifth National Mental Health and Suicide Prevention Plan (2017–2022); the National Mental Health and Suicide Prevention Agreement; Vision 2030 for mental health reform; and the National Mental Health Policy National Mental Health Workforce Strategy (2022–2032) (5,7). Highlighting mental health as a national priority, the government also instituted a major Productivity Commission Inquiry into mental health in Australia (7). To improve mental healthcare access, policies developed were targeted at financial and geographic barriers (8). The rapid pace of policy changes and the time lag necessary for results create methodological challenges for isolating effects. Given these complexities, there is a dire need for an empirical study to examine and highlight the effects associated with bulk billing policy reforms. This study aims to highlight bulk billing changes from 2019 to 2023 and assess associated effects on mental health service use and disparities across states and territories. By focusing on fully subsidised Medicare mental health in-person services, partially subsidised Medicare mental health services, and telehealth mental health services, this study seeks to provide a clearer understanding of policy effects towards guiding future reforms.

Brief Review of Literature Highlights of Bulk Billing Policy Changes (2019–2023) The bulk billing policy changes developed under the Medicare Benefits Scheme (MBS) from 2019 to 2023 were designed to improve access to services, promote equity, and protect from catastrophic health expenditure, especially for vulnerable populations (5,9). These include: Introduction of temporary telehealth bulk billing incentives during COVID-19 towards ensuring continued access in March 2020, especially for mental health care.<br /> Addition of extra incentives in April 2020, towards encouraging bulk billing for vulnerable populations, including children younger than 16 years of age, pensioners, and concession cardholders, April 2020. July 2021 scaling back of telehealth incentives, enabling health providers to charge fees; and Tripling of bulk billing incentives in November 2023 for vulnerable populations towards reversing the decline in service use. While these policy reforms were dynamic and reactive, they aimed at addressing immediate challenges. These include the COVID-19 pandemic, the rise in operational costs, and declining use of services (5,9,10).

Theoretical Framework The Andersen’s Behavioural Model of Health Service Use is a widely used framework in the literature (11,12)was used to assess the use of mental health services was applied in this study. The model considers the factors that predispose to (e.g., age, gender)(13), enable (e.g., policy changes, affordability), and influence (e.g., severity of mental health conditions) the use of health services (14). This model provides a guide to the analysis of Medicare bulk-billing policy reforms and their effects on the use of mental health services in Australia (Figure 1). Mental health is a key focus of bulk billing reforms (7,8), prompting the need to assess how policies have influenced (6) pressure on the healthcare system(15), out-of-pocket expenses, and service use.

Figure 1: A Conceptual Framework illustrating the Effects of Bulk Billing Policy Changes on the Use of Mental Health Services. (Based on Andersen’s Behavioural Model of Health Services Utilisation)

Author Update (June 2026)

Following a subsequent audit of this preprint, several references and supporting citations were identified as inaccurate. The manuscript was later comprehensively revised, references were re-verified against original sources, and corrections were incorporated into a revised version submitted subsequently. Readers are advised to consult the annotations within this preprint regarding affected citations.

Hes noticed that romeos behavior has caused him to keep him awake at night

Romeo tends to find juliets eyes the most beautiful eyes that the world hasent seen

Romeos Craving for juliet is extremely strong

I appreciate John coming to this conclusion based on his long experience and deep thinking about sustainable communities and food systems.

zijn gevolgen van gedrag of productie die terechtkomen bij anderen zonder dat die gevolgen in de prijs zijn meegenomen

betekent dat een maatregel niet zwaarder mag zijn dan nodig om het doel te bereiken

betekent dat de overheid pas moet ingrijpen als een probleem niet goed door burgers, bedrijven of lagere overheden zelf kan worden opgelost

betekent dat een maatregel efficient is als de winnaars genoeg voordelen hebben om de verliezers te kunnen compenseren

achteraf kijken naar de gevolgen en aansprakelijkheid

achteraf kijken naar de gevolgen en aansprakelijkheid

I think this is interesting that this pluralistic approach to provenance ends up mapping relationships between parties of interest rather than solely the relationships between parties and the records. In the minds of most people, archival or memory work seems to be one of primarily capture, a recording of some phenomenon, endogenous with respect to the individual/institution that is recording. And in the case of colonial violence, there is very much an incentive for the instigator of the violence, not to record the violence as brutal violence. I imagine a lot of lost history or the lack of documentation of this violence is done behind closed doors, or perpetrated through under-the-table interactions. So, mapping the relationships between the perpetrators of imperial violence and the colonized will in fact act as a kind of indicator or a kind of trace that something did happen or sprung up out of this particular relationship.

Súmula Vinculante 31 - É inconstitucional a incidência do Imposto sobre Serviços de Qualquer Natureza – ISS sobre operações de locação de bens <u>móveis</u>.

yeah, trying to make a one-size fits all design is pretty unlikely, everyone has a complaint, no one can be permanently satisfied.

I agree with the video and how it shows what makes a site good and what is more important than other things. I agree with the idea of having content split into different categories to make the app more understandable

I like how he explained the fourth reason on how audience based website navigation can reduce a users trust in the site and how when users buying a laptop have 2 options, for personal and for business will get the same results but different pricing. If that was be buying a product and it has different pricing under different section I would not find it open and honest.

I think I understand why segmenting between business and user is bad in some ways. A business sole website should focus primarily their user base. If there was navigation for businesses then you wouldn't have a brand that serves to customers but instead business to business. The money comes from the customer base. Designers need to account what the business MAIN AUDIENCE, and figure out their pain points.

The speaker in this video talks about how segmenting a websites navigation by audience categories usually degrades the user experience, because the same information will be found in multiple categories and users may not feel as though they fit in one specific category. It should only be done in cases where there is NO overlap and each section had information that is only in that section. My takeaway is that developers and designers should consider how user needs can be best met and design a system accordingly.

While categorizing can be difficult, ecspecially when dealing with a website that has large content, it is very important to spend time to develop a good plan on how to separate the content. However, this can prove to be difficult when you're dealing with multipe user types that have overlapping content needs.

The video showed some key details to what a site needs and overall what the site should present to the audienece,. Having everythng in categories like the example in the video was a healthcare site where eveyrthing had a category for each topic that will help the patient or customer navigate through the site and see exactly what they are looking for.

Having content split into category's may seem like an optimal way of pushing users into being in the correct places, but this can often lead to users missing important information that may not be listed under their category, or have users be forced to go through repetitive content if they fit under more than one category.

I agree that it is impossible to create a "one size fits all" page. In the healthcare site example, I was not aware that people accessed pages meant for doctors and healthcare providers. While I understand their wanting to be proactive, I wonder if that would instead be counter productive due to them regurgitating information they might not understand

As UX designers, it’s often smarter to make the executive decisions for our users and build a unified experience, rather than trying to satisfy too many different niches on a single site. A general website that works well for the majority is much more effective than a hyper-specific one with no clear focus. Just like we use different physical tools for different tasks, distinct audiences are often better served by entirely different websites altogether.

The first takeaway is that when first visit a website that has a lot of people visiting to it, they often don't self identify into one single audience group, they go into whatever group or groups suit them the best or don't go into any groups. The second takeaway is that there will often content between different groups often overlaps with another groups which is a common issue for navigation-based websites. The third takeaway is sometimes people may assume that certain information is mention for a certain group which can lead to the useful information never being used and ignored.

I think this can make users trust a website less. In the video, the same computer was listed under both the Home and Business categories, but it was more expensive in the Business section because businesses usually aren't as price sensitive. If users notice that, it can feel a little misleading and make them question the site's pricing.

The video makes audience based website navigation easy to understand by describing the different aspects. With the speaker going in depth on some details, such as categories, the idea of designing website is a lot more than just looks. Looks can be targeted at different groups, and it is important to be aware of who your audience is when designing a website.

Organizing different categories should be its own tab and not found in the home tab and the business tab because the business tab will be more expensive. Audience based website is organizing it for who is visiting the site

What stood out to me the most was how the video showed that small decisions or actions can have a bigger impact than people might expect. It made me think about things differently and helped me understand the topic on a deeper level. I also liked that the information was explained in a straightforward way without making it overly complicated. Overall, it was interesting and gave me a few ideas that I had not considered before.

While structuring a website by audience seems intuitive, it usually fails because users rarely fit into neat categories and often look for information outside their assigned group. This approach also creates duplicate content, buries actual topics deeper into the site, and can even damage user trust. Instead, it is much better to organize your navigation by topic or task, reserving audience specific-sections only for content with absolutely zero overlap.

I found it to be interesting that some users will look under the section intended for doctors in order to find more info. while simplifying a category because of a pre-conceived belief about the user (such as a lack of medical knowledge) can seem like a good idea, if often leaves the user half informed and at risk of making un-informed decisions.

This article on The Pace of Modern Life is interesting because to me, the pace of life is a very individualistic thing. In terms of technology rapidly developing and AI rising quickly from the surface, its easy to think our world is moving so fast with those changes. And it is, however, I also find it hard that that determines the pace of life. But when reading this article, it refelcts alot on different generations and their imrpovments, and now does feel especially fast in the world of booming technology and things being all online and social media having such a large presence.

This article argues that the greatest risk of AI is not that it will become uncontrollable, but that companies will use it to justify decisions that increase profits while avoiding responsibility for harm to workers and society. He contends that whether AI benefits people or worsens inequality depends on the political and economic systems governing its use, not on the technology itself.

The Ted Chiang piece linked in 21.2 is great to check out along with this chapter. Chiang says that AI operates more like a management consulting firm rather than just a new tech thing. It’s used by companies to exploit workers while putting the smart decision-making onto "the algorithm." So, the real issue here isn’t that the tech itself is evil; it’s about control and motivation. This fits well with the chapter on capitalism. Think about it: Facebook's parent company, Meta, works in much the same way. The Luddites weren’t opposed to all technology; they protested machines that cost them jobs. Getting this distinction right matters a bunch for figuring out AI ethics now.

This is super scary. I know we accept a certain level of lacking privacy, but the data that they have and we don’t realize could really be used for malice intent or just generally it invades users’ privacy.

Data Feminism introduces a new framework for data science and ethics rooted in the principles of intersectional feminism to address power imbalances in technology. The authors argue that data are never neutral and provide strategies for scientists to use their work to challenge existing structures of injustice.

In one of my previous classes, GEOG 258: Digital Geographies, they showed us this film. The film shows how technologies such as facial recognition and large language models and how they promote inequality. One example in the film is that facial recognition software is much less effective on people of color than white people, because that is the biases behind the people who created it.

I feel like I am much more conscious and aware about what I do and how I am being perceived online through the lens of these massive corporations. Additionally, with the information I have learned, I am also far more conscious about the ethical complications and complexities of these situations

My views on social media have definitely been reinforced in the sense that bots and automated processes have a lot of presence in that realm. I didn't really realize it because It isn't really the type of thing that I interact with most, but taking this class, I've realized that social media actions such as harassment, trolling, and other algorithms are often produced by bots.

I have never had any type of ethics class, so all of this information was new to me. I did not know about how you use an ethics framework to decide if any actions are ethical or not, and I found that very interesting. I also learned about many different ethics frameworks, some that I have never heard of before.

Rapport de Synthèse : La Justice de la Famille et des Mineurs au Tribunal de Nanterre

Ce document présente une analyse détaillée des fonctions et des enjeux de la justice aux affaires familiales et de la justice des enfants, telle qu'observée au sein du Tribunal de Grande Instance (TGI) de Nanterre.

Résumé Analytique

La justice familiale et juvénile repose sur un principe cardinal : l'intérêt supérieur de l'enfant.

À Nanterre, cette mission est portée par des magistrats, majoritairement des femmes, qui traitent une charge de travail massive caractérisée par des audiences marathon et une gestion constante de conflits humains profonds.

Le Juge aux Affaires Familiales (JAF) intervient comme un arbitre dans les ruptures de couple, tranchant les litiges relatifs à la garde, aux pensions et aux droits de visite.

Parallèlement, le Juge des Enfants déploie une "justice éducative" qui oscille entre la sanction pénale pour les mineurs délinquants et l'assistance éducative pour les familles en difficulté.

L'efficacité de cette justice repose sur la capacité des magistrats à se détacher de l'émotion pour rendre des décisions ayant un impact définitif sur la vie des citoyens, tout en faisant face à une saturation institutionnelle et un renouvellement fréquent des effectifs.

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I. Le Juge aux Affaires Familiales (JAF) : L'Arbitre du Conflit Privé

Missions et Principes de Décision

Le JAF statue sur les divorces, les pensions alimentaires et la résidence des enfants.

Chaque décision est guidée par l'intérêt de l'enfant, cherchant souvent à pacifier les relations par le biais de procès-verbaux d'acceptation de rupture, évitant ainsi la recherche de responsabilité.

| Aspect | Détails de la Fonction | | --- | --- | | Volume de travail | Environ 100 dossiers par mois par juge ; jusqu'à 20 audiences par jour. | | Profil des juges | À Nanterre, le pôle famille est composé de 9 femmes. | | Critère de jugement | L'intérêt de l'enfant prime sur les griefs entre parents. | | Outils de décision | Rapports d'enquête sociale, expertises psychologiques, médiation. |

Thématiques Récurrentes et Contentieux

Le document identifie trois types de litiges majeurs traités par le JAF :

• La Résidence des Enfants : Les parents s'opposent souvent sur la capacité de l'autre à s'occuper des enfants.

Le juge privilégie fréquemment la résidence alternée pour maintenir un équilibre, malgré les craintes des parents concernant la divergence des modèles éducatifs.

• Le Conflit Financier : La pension alimentaire est décrite comme "le nerf de la guerre".

Le juge doit trancher sur le maintien, la diminution ou la suppression des pensions, même pour des enfants majeurs, en rappelant l'obligation d'entretien jusqu'à l'autonomie.

• L'Aliénation et la Coparentalité : Dans les situations de "guerre de tranchée", le juge constate souvent une rupture de communication.

L'enquête sociale révèle parfois des discours parentaux dégradés qui exposent les enfants à des pressions psychologiques.

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II. Le Juge des Enfants : Entre Sanction et Protection

Le Juge des Enfants assure une double mission : pénale (sanctionner les délits) et civile (assistance éducative).

La Justice Pénale des Mineurs

L'objectif est de créer un "électrochoc" chez le mineur pour favoriser une prise de conscience.

La justice pénale s'applique dès 13 ans, avec une spécificité : l'excuse de minorité réduit les peines encourues de moitié par rapport aux adultes.

• Profil de la délinquance : Vols avec violence, recel, trafic de stupéfiants.

Les prévenus ont en moyenne 16 ans.

• Types de sanctions :

  • Travaux d'intérêt général (TIG).

• Surcis avec mise à l'épreuve (souvent assorti d'une obligation de formation ou de travail).

• Détention provisoire dans les cas graves ou de récidive immédiate.

• Résultats : 65 % des jeunes condamnés ne récidivent pas.

La délinquance des mineurs est en légère diminution depuis 2010.

L'Assistance Éducative

Cette mission occupe 70 % du temps des juges des enfants.

Elle vise à accompagner les familles dont les enfants sont en danger ou dont l'équilibre est perturbé par le conflit parental ou la violence.

• Rôle des éducateurs : Suivre le développement de l'enfant et conseiller les parents.

• Risques identifiés : Les juges avertissent que les enfants exposés à des conflits massifs risquent, à l'adolescence, un rejet total de la famille, une révolte ou des conduites à risque.

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III. Les Acteurs Clés et l'Organisation Judiciaire

Le Parquet (Le Procureur)

Le procureur (comme Stéphanie Dian) est l'interlocuteur direct des commissariats.

Il décide des suites à donner aux signalements :

• Classement sans suite : Notamment en l'absence de contrainte ou de violence caractérisée.

• Défèrement : Présentation immédiate devant un juge après la garde à vue pour les faits graves.

• Prolongation de garde à vue : Utilisée pour finaliser les enquêtes.

La Réalité Matérielle et Psychologique du Magistrat

Le document souligne la pénibilité de la fonction :

• Fatigue et turnover : La répétition des audiences et la charge de rédaction permanente induisent un renouvellement fréquent des juges.

• Engagement émotionnel : Les juges doivent maintenir une écoute active et être réactifs tout en gérant l'agressivité verbale fréquente lors des audiences.

• Le "Juge Rapporteur" : En raison du manque d'effectifs, certaines audiences de filiation ou d'état civil se tiennent devant un seul juge au lieu de trois.

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IV. Citations et Témoignages Significatifs

• Sur la responsabilité du JAF : "Si pour nous c'est un dossier parmi d'autres... pour les personnes qui vont la lire, c'est la décision peut-être de leur vie." — Marie-Catherine Gaffinel.- Sur la médiation : "Une décision de justice elle a pas vocation à réglementer tous les petits aléas de la vie courante." — Marie-Catherine Gaffinel.- Sur la délinquance juvénile : "Le rôle des juges des enfants n'est pas uniquement de sanctionner mais de faire prendre conscience à ces jeunes de la gravité des faits."- Témoignage d'un parent : "On a l'impression d'avoir été coupable du fait qu'il soit ici d'avoir peut-être oui loupé quelque chose." — Le père de Matthéo.

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V. Cas Particuliers de l'État Civil

Au-delà des conflits, le tribunal traite des demandes liées à l'intégration et à la filiation :

• Changement de prénom : Requêtes visant à ajouter un prénom français pour faciliter la vie quotidienne et professionnelle, tout en conservant les racines d'origine.

• Contestation de paternité : Actions visant à rétablir la vérité biologique, souvent longues et nécessitant des tests de paternité ordonnés par le juge.

Justice aux Affaires Familiales et des Enfants : Analyse des Pratiques et Enjeux Judiciaires

Résumé Exécutif

Ce document de synthèse analyse les interventions des juges aux affaires familiales (JAF) et des juges des enfants, telles que décrites dans le contexte source.

L'action judiciaire s'articule autour d'un principe cardinal : l'intérêt supérieur de l'enfant.

Que ce soit dans le cadre de séparations conflictuelles, de la protection de mineurs en danger ou de la délinquance juvénile, le magistrat agit comme un arbitre de l'intime, cherchant à concilier sanction, éducation et préservation des liens familiaux.

Les points clés incluent :

• La primauté de l'intérêt de l'enfant dans toutes les décisions de garde et de protection.

• L'importance de la parole de l'enfant, recueillie de manière spécifique pour éclairer le juge sans lui imposer une décision.

• La double mission du juge des enfants, oscillant entre l'assistance éducative (70 % de l'activité) et la réponse pénale à la délinquance.

• La gestion des conflits parentaux persistants, où le juge doit souvent imposer des médiations ou des enquêtes sociales pour pallier la rupture de communication.

• La charge de travail et la pression émotionnelle pesant sur les magistrats, confrontés à des volumes de dossiers importants et à des situations humaines critiques.

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I. La Justice aux Affaires Familiales (JAF) : L'Arbitrage des Ruptures

Le juge aux affaires familiales intervient principalement lors des séparations de couples (mariés, pacsés ou en concubinage) pour organiser les conséquences de la rupture.

Modalités de Garde et de Résidence

La question de la résidence des enfants est le point de friction majeur.

Le contexte présente deux modèles principaux :

• La résidence alternée : Souvent privilégiée par les juges pour maintenir le lien avec les deux parents, même en cas de désaccord initial.

Elle est vue comme un moyen d'éviter qu'un enfant ne soit "contre l'autre pour pouvoir se positionner".

• La garde exclusive : Demandée en cas d'allégations de violence (physique ou psychologique), de négligence ou de comportements inadaptés (ex: religion exacerbée, emportements verbaux).

Outils de Décision et d'Apaisement

Face aux versions contradictoires des parents, les juges disposent de plusieurs leviers :

• L'enquête sociale : Ordonnée pour vérifier les conditions de vie et les reproches mutuels.

• La médiation familiale : Orientée par le juge pour tenter de rétablir une communication constructive et "faire appel au sens des responsabilités" des parents.

• L'expertise psychologique : Utilisée pour identifier des phénomènes tels que l'aliénation parentale ou les discours de façade.

Enjeux Financiers et État Civil

Le JAF traite également le "nerf de la guerre" : l'argent.

• Pensions alimentaires : Fixées selon les revenus et les besoins, leur maintien ou suppression fait l'objet de débats houleux, même pour des enfants majeurs encore dépendants.

• État civil : Les audiences couvrent aussi des demandes de changement de prénom (pour intégration ou commodité administrative) ou des contestations de paternité nécessitant des tests biologiques.

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II. La Parole de l'Enfant : Un Avis Consultatif Fondamental

La loi permet l'audition des mineurs par le juge, une pratique structurée pour protéger l'enfant tout en recueillant son ressenti.

| Aspect | Procédure et Observations | | --- | --- | | Âge requis | Pas de minimum légal, mais généralement à partir de 8 ans (ex: 300 enfants entendus par an à Montpellier). | | Accompagnement | L'enfant est assisté par un avocat dédié, neutre vis-à-vis des parents. | | Confidentialité | L'audition n'est pas publique. Le juge peut "adoucir" certains propos dans son rapport pour ne pas nuire à la relation enfant-parent. | | Poids de la parole | L'avis est consultatif. Le juge doit déceler si l'enfant est manipulé ou s'il exprime un besoin réel (ex: désir de rejoindre son père par manque affectif). |

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III. La Protection de l'Enfance et l'Assistance Éducative

Le juge des enfants consacre la majorité de son temps (environ 70 %) à protéger les mineurs dont la sécurité, la santé ou la moralité sont en danger.

Interventions Protectrices

• Suivi éducatif : Mise en place d'éducateurs pour conseiller les parents et surveiller le développement de l'enfant dans des contextes de conflits massifs.

• Tutelles et Administration légale : Gestion du patrimoine des mineurs orphelins (ex: héritage de 80 000 €) pour éviter que les fonds ne soient dilapidés à la majorité.

• Délaissement parental : Dans les cas extrêmes (absence de nouvelles des parents pendant des années), le juge peut déclarer un enfant "adoptable" (statut de pupille).

Cas Critiques : Retours de Zones de Guerre

Le document souligne des situations d'une gravité exceptionnelle, comme le retrait total de l'autorité parentale pour des pères ayant emmené leurs enfants en Syrie.

Les enfants, témoins de scènes de guerre et de décapitations, sont protégés par l'institution qui doit gérer leur traumatisme et leur peur du retour du parent.

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IV. La Justice Pénale des Mineurs : Sanctionner et Éduquer

La justice des mineurs s'applique dès 13 ans en France, avec un double objectif de sanction et de prise de conscience.

Typologie des Délits

Les dossiers fréquents incluent :

• Vols avec violence (souvent commis en groupe).

• Trafic et usage de stupéfiants (cannabis).

• Recels (scooters).

Réponse Pénale et Graduation

Les magistrats cherchent à créer un "électrochoc" par la solennité de l'audience.

Les peines mentionnées sont :

• Mesures éducatives : Travaux d'intérêt général (TIG), stages de citoyenneté, rappels à la loi.

• Sursis avec mise à l'épreuve : Peines de prison (ex: 2 à 6 mois) qui ne sont exécutées qu'en cas de récidive, obligeant le jeune à trouver un travail ou une formation.

• Détention provisoire : Décidée pour les cas de récidive grave ou de violence sur personnes vulnérables (ex: agression d'une personne de 80 ans).

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V. Réalité et Contraintes de la Profession

Le rôle de magistrat dans ces chambres spécialisées comporte des exigences humaines et administratives lourdes.

• Audiences Marathons : Les juges peuvent traiter jusqu'à 20 dossiers par jour, nécessitant une réactivité et une écoute constantes.

• Charge Émotionnelle : Confrontation permanente à la détresse humaine, à la violence et aux secrets intimes.

"C'est fatigant... il faut être de bonne humeur, il faut être à l'écoute."

• Turnover Important : La fatigue liée au rythme soutenu de rédaction et d'audience entraîne un renouvellement fréquent des effectifs.

• Responsabilité Sociale : Les juges ont conscience que leurs décisions, bien que "dossiers parmi d'autres" pour l'institution, sont souvent "la décision de leur vie" pour les justiciables.

Citation clé : "L'important c'est de pouvoir en tant que juge stigmatiser ce qui dysfonctionne mais on ne juge que les actes on ne juge pas les personnes." — Juge Magalie Jiménez.

Eli Whitney's story really hits home for me. He believed his cotton gin would decrease slavery because it was less labor-intensive. Instead, it had the opposite effect — it made slave-grown cotton super profitable, which increased the demand for slaves. It's a real wake-up call that good intentions aren't enough; you need to consider the ethical outcomes too. As a finance student, I often think about how money goes wherever the returns are best, no matter the cost to people. So, the takeaway isn't to stop innovating, but to think through the possible consequences beforehand.

This is kind of funny and scary at the same time. We see the rollout of these new models of AI that are going to make our lives better and better, until it starts to take away from the human experience and be used for worse and worse avenues of destruction.

I will say my ability to identify intentionally inflammatory accounts has become significantly better. But the more interesting part is I have found myself wondering why they are having inflammatory accounts.

something we arent talking about is the fact that we measure gains in rating alignemnt as well. note that after the model's predictions flip from A -> R it starts spitting out weaknesses and novelty critiques at a higher rate, agreeing with human critique.

this sentence is way too long, you need to cut it down. also no one knows what newtowrk ties and halo effects are please fix that.

OR-ICLR

OR-ICLR!

OR-ICLR

hanging line

OR-ICLR

hanging line

please center the numbers in the columns also make delta Acc on a new line, can we decrease the width of this table, so that the figure B can be larger?

OR-ICLR

OR-ICLR

OR-ICLR here too

please read LLamaFactoryAutoReviewer/results/frontier_memory_probe/breakdown.md., please add the results to the appendix section that talks about frotneir models and then reference that here. show a summarized table view here that clearly shows that the frontier models consistently fail at the different recall tests. highest on 24. add a short paragraph here.

this should be OR-ICLR

OR-ICLR

OR-ICLR

lets create a new paragraph header called: "Baseline Correction Measures."

lets name this Baseline Prompting.

use period here

delete colon and replace with period

delete

here i think we can say OR-ICLR

because they are inherently calibrated to the test distribtuion, since our training data has the test years (or somethign like that).

instead of probes lets call these "models". also lets name the paragraph: "Tabular feature analysis."

for the figure 4 feature prob audit, you state full feature definitions are in table c.1, but really it should be appendix c like in the main text. also you were supposed to raise the legend [accept/reject] and change the naems to OR-ICLR and arXiv. please fix (in the right subfigure) you corrected the table

the table should state OR-ICLR and arXiv in italics, and then center the columns for T/V

hanging line

OR-ICLR in the dataset coverage image

datasets

add (OR-ICLR) here

hanging line please fix

In contrast with prior work (cite), reviews, ratings, and meta-reviews are not regressed on; they are only used for measuring rating correlation and review alignment.

add section reference here

maybe add:

https://www.cell.com/molecular-therapy-family/nucleic-acids/fulltext/S2162-2531(24)00028-3

https://www.sciencedirect.com/science/article/pii/S0753332225006468

it might be worth including, but might be too far off the path, that publication kinda explains the path that we took with the CD4 specific mouse

severin et al 2016 https://academic.oup.com/brain/article/139/6/1747/1754080?login=true

see previous comment. I will work on the manuscript later today, so there is an updated version of it available

the references are not accessible through that google doc. let me know if that had something to do with my comments and running it through my office

maybe just write miR-128

and EBV ;)

typo remyelinating

typo challenges

MS

typo demyelinating

We hebben hiermee nog geen vangnet voor een Patient die is aangemaakt, nooit een Task heeft gekregen en dus ook nooit is gelauncht...