444 Matching Annotations
  1. Mar 2021
    1. 2021-02-10

    2. Spiro, Neta, Rosie Perkins, Sasha Kaye, Urszula Tymoszuk, Adele Mason-Bertrand, Isabelle Cossette, Solange Glasser, and Aaron Williamon. ‘The Effects of COVID-19 Lockdown 1.0 on Working Patterns, Income, and Wellbeing Among Performing Arts Professionals in the United Kingdom (April–June 2020)’. Frontiers in Psychology 11 (2021). https://doi.org/10.3389/fpsyg.2020.594086.

    3. 10.3389/fpsyg.2020.594086
    4. This article reports data collected from 385 performing arts professionals using the HEartS Professional Survey during the COVID-19 Lockdown 1.0 in the United Kingdom. Study 1 examined characteristics of performing arts professionals’ work and health, and investigated how these relate to standardized measures of wellbeing. Study 2 examined the effects of the lockdown on work and wellbeing in the respondents’ own words.
    5. The Effects of COVID-19 Lockdown 1.0 on Working Patterns, Income, and Wellbeing Among Performing Arts Professionals in the United Kingdom (April–June 2020)
    1. 2021-02-01

    2. Spagnoli, Paola, Carmela Buono, Liliya Scafuri Kovalchuk, Gennaro Cordasco, and Anna Esposito. ‘Perfectionism and Burnout During the COVID-19 Crisis: A Two-Wave Cross-Lagged Study’. Frontiers in Psychology 11 (2021). https://doi.org/10.3389/fpsyg.2020.631994.

    3. 10.3389/fpsyg.2020.631994
    4. The current study aims at examining the relationship between the perfectionism two-factor model (i.e., concerns and strivings) and burnout dimensions measured by using the BAT (Burnout Assessment Tool) through a longitudinal study. A two-wave cross-lagged study was conducted using path analysis in SEM (Structural Equation Modeling) of 191 workers. Results confirmed the predictive role of perfectionistic concerns on the burnout dimensions, whereas perfectionistic strivings were not significantly related, suggesting that perfectionism should be monitored by employers and clinicians to prevent employee burnout. Limitations and future research directions are envisaged.
    5. Perfectionism and Burnout During the COVID-19 Crisis: A Two-Wave Cross-Lagged Study
    1. 2021-03-26

    2. Rosen, M. L., Rodman, A. M., Kasparek, S. W., Mayes, M., Freeman, M. M., Lengua, L., … McLaughlin, K. A., PhD. (2021, March 26). Promoting youth mental health during COVID-19: A Longitudinal Study spanning pre- and post-pandemic. https://doi.org/10.31234/osf.io/n5h8t

    3. 10.31234/osf.io/n5h8t
    4. The COVID-19 pandemic has introduced many novel stressors into the lives of youth. Identifying factors that protect against the onset of psychopathology in the face of these pandemic-related stressors is critical. We examine a wide range of factors that may protect youth from developing psychopathology during the pandemic. We assessed pandemic-related stressors, internalizing and externalizing psychopathology, and potentially protective factors in a longitudinal sample of children and adolescents (N=224, 7-15 years) assessed prior to the pandemic, during the stay-at-home orders, and six months later. We evaluate how family behaviors during the stay-at-home orders are related to changes in psychopathology relative to the assessment prior to the pandemic, identify factors that moderate the association of pandemic-related stressors with psychopathology, and determine whether these associations vary by age. Higher exposure to pandemic-related stressors was associated with increases in both internalizing and externalizing symptoms and this association persisted six months later. Greater engagement in exercise, a structured routine, greater time spent in nature, and less screen time, were associated with reduced internalizing and externalizing problems. The association between pandemic-related stressors and increases in psychopathology was significantly lower for youths with limited passive screen time, and marginally so for those with more time outdoors. The strong association between pandemic-related stressors and internalizing symptoms was absent among children, but not adolescents, with lower news media consumption related to the pandemic both concurrently and prospectively. We provide insight into simple and practical steps that families can take that may promote resilience against mental health problems in youth during the COVID-19 pandemic and protect against psychopathology following pandemic-related stressors.
    5. Promoting youth mental health during COVID-19: A Longitudinal Study spanning pre- and post-pandemic
    1. 2021-03-22

    2. Holden Thorp, H. (2021) ‘No Senator, It’s Not Theater | Editor’s Blog’. https//blogs.sciencemag.org/editors-blog/2021/03/22/no-senator-its-not-theater/.

    3. I grew up in the theater. My mom ran the community theater in my hometown and I was the de facto operations person. Theater is a very specific activity. There’s a script, there’s a production plan, and there are deep psychodynamics brought out through dialogue. Theater gives us important ideas and characters like Arthur Miller’s Willy Loman, Tennessee Williams’s Amanda Wingfield, or Lorraine Hansberry’s Walter Younger. Theater is not someone deciding to wear a mask to support public health during a pandemic.
    4. No Senator, it’s not theater
    1. 2021-03-25

    2. Karimi, Fariba, and Petter Holme. ‘A Temporal Network Version of Watts’s Cascade Model’. ArXiv:2103.13604 [Physics], 25 March 2021. http://arxiv.org/abs/2103.13604.

    3. 2103.13604
    4. Threshold models of cascades in the social sciences and economics explain the spread of opinion and innovation due to social influence. In threshold cascade models, fads or innovations spread between agents as determined by their interactions with other agents and their personal threshold of resistance. Typically, these models do not account for structure in the timing of interaction between the units. In this work, we extend a model of social cascades by Duncan Watts to temporal interaction networks. In our model, we assume friends and acquaintances influence agents for a certain time into the future. That is the influence of the past ages and becomes unimportant. Thus, our modified cascade model has an effective time window of influence. We explore two types of thresholds -- thresholds to fractions of the neighbors or absolute numbers. We try our model on six empirical datasets and compare them with null models.
    5. A temporal network version of Watts's cascade model
    1. 2021-03-25

    2. Holme, Petter, and Jari Saramäki. ‘Temporal Networks as a Modeling Framework’. ArXiv:2103.13586 [Physics], 24 March 2021. http://arxiv.org/abs/2103.13586.

    3. 2103.13586
    4. To understand large, connected systems, we cannot only zoom into the details. We also need to see the large-scale features from afar. One way to take a step back and get the whole picture is to model the systems as a network. However, many systems are not static, but consisting of contacts that are off and on as time progresses. This Chapter introduces the mathematical and computational modeling of such systems and thus an introduction to the rest of the book. We will cover some of the earlier developments that form the foundation for the more specialized topics of the other Chapters.
    5. Temporal networks as a modeling framework
    1. 2021-03-26

    2. Monod, Mélodie, Alexandra Blenkinsop, Xiaoyue Xi, Daniel Hebert, Sivan Bershan, Simon Tietze, Marc Baguelin, et al. ‘Age Groups That Sustain Resurging COVID-19 Epidemics in the United States’. Science 371, no. 6536 (26 March 2021). https://doi.org/10.1126/science.abe8372.

    3. 10.1126/science.abe8372
    4. After initial declines, in mid-2020, a sustained resurgence in the transmission of novel coronavirus disease (COVID-19) occurred in the United States. Throughout the US epidemic, considerable heterogeneity existed among states, both in terms of overall mortality and infection, but also in the types and stringency of nonpharmaceutical interventions. Despite these stark differences among states, little is known about the relationship between interventions, contact patterns, and infections, or how this varies by age and demographics. A useful tool for studying these dynamics is individual, age-specific mobility data. In this study, we use detailed mobile-phone data from more than 10 million individuals and establish a mechanistic relationship between individual contact patterns and COVID-19 mortality data.
    5. Age groups that sustain resurging COVID-19 epidemics in the United States
    1. 2020-07-14

    2. Ioannidis, John P. A. (2020) ‘The Infection Fatality Rate of COVID-19 Inferred from Seroprevalence Data’. MedRxiv. https://doi.org/10.1101/2020.05.13.20101253.

    3. 10.1101/2020.05.13.20101253
    4. Objective To estimate the infection fatality rate of coronavirus disease 2019 (COVID-19) from data of seroprevalence studies.Methods Population studies with sample size of at least 500 and published as peer-reviewed papers or preprints as of July 11, 2020 were retrieved from PubMed, preprint servers, and communications with experts. Studies on blood donors were included, but studies on healthcare workers were excluded. The studies were assessed for design features and seroprevalence estimates. Infection fatality rate was estimated from each study dividing the number of COVID-19 deaths at a relevant time point by the number of estimated people infected in each relevant region. Correction was also attempted accounting for the types of antibodies assessed. Secondarily, results from national studies were also examined from preliminary press releases and reports whenever a country had no other data presented in full papers of preprints.Results 36 studies (43 estimates) were identified with usable data to enter into calculations and another 7 preliminary national estimates were also considered for a total of 50 estimates. Seroprevalence estimates ranged from 0.222% to 47%. Infection fatality rates ranged from 0.00% to 1.63% and corrected values ranged from 0.00% to 1.31%. Across 32 different locations, the median infection fatality rate was 0.27% (corrected 0.24%). Most studies were done in pandemic epicenters with high death tolls. Median corrected IFR was 0.10% in locations with COVID-19 population mortality rate less than the global average (<73 deaths per million as of July 12, 2020), 0.27% in locations with 73-500 COVID-19 deaths per million, and 0.90% in locations exceeding 500 COVID-19 deaths per million. Among people <70 years old, infection fatality rates ranged from 0.00% to 0.57% with median of 0.05% across the different locations (corrected median of 0.04%).Conclusions The infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case-mix of infected and deceased patients as well as multiple other factors. Estimates of infection fatality rates inferred from seroprevalence studies tend to be much lower than original speculations made in the early days of the pandemic.
    5. The infection fatality rate of COVID-19 inferred from seroprevalence data
    1. 2021-03-23

    2. Calgary, Open. ‘COVID-19 Case Surveillance Public Use Data with Geography | Data | Centers for Disease Control and Prevention’. Accessed 26 March 2021. https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data-with-Ge/n8mc-b4w4.

    3. The COVID-19 case surveillance database includes patient-level data reported by U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as "immediately notifiable, urgent (within 24 hours)" by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020 to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data collected by jurisdictions are shared voluntarily with CDC.For more information, visit: wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/case-definition/2020/08/05/.
    4. COVID-19 Case Surveillance Public Use Data with Geography
    1. 2021-03-08

    2. Gray, Kathryn J., Evan A. Bordt, Caroline Atyeo, Elizabeth Deriso, Babatunde Akinwunmi, Nicola Young, Aranxta Medina Baez, et al. ‘COVID-19 Vaccine Response in Pregnant and Lactating Women: A Cohort Study’. MedRxiv: The Preprint Server for Health Sciences, 8 March 2021. https://doi.org/10.1101/2021.03.07.21253094.

    3. 10.1101/2021.03.07.21253094
    4. Background: Pregnant and lactating women were excluded from initial COVID-19 vaccine trials; thus, data to guide vaccine decision-making are lacking. We sought to evaluate the immunogenicity and reactogenicity of COVID-19 mRNA vaccination in pregnant and lactating women. Methods: 131 reproductive-age vaccine recipients (84 pregnant, 31 lactating, and 16 non-pregnant) were enrolled in a prospective cohort study at two academic medical centers. Titers of SARS-CoV-2 Spike and RBD IgG, IgA and IgM were quantified in participant sera (N=131), umbilical cord sera (N=10), and breastmilk (N=31) at baseline, 2nd vaccine dose, 2-6 weeks post 2nd vaccine, and delivery by Luminex, and confirmed by ELISA. Titers were compared to pregnant women 4-12 weeks from native infection (N=37). Post-vaccination symptoms were assessed. Kruskal-Wallis tests and a mixed effects model, with correction for multiple comparisons, were used to assess differences between groups. Results: Vaccine-induced immune responses were equivalent in pregnant and lactating vs non-pregnant women. All titers were higher than those induced by SARS-CoV-2 infection during pregnancy. Vaccine-generated antibodies were present in all umbilical cord blood and breastmilk samples. SARS-CoV-2 specific IgG, but not IgA, increased in maternal blood and breastmilk with vaccine boost. No differences were noted in reactogenicity across the groups. Conclusions: COVID-19 mRNA vaccines generated robust humoral immunity in pregnant and lactating women, with immunogenicity and reactogenicity similar to that observed in non-pregnant women. Vaccine-induced immune responses were significantly greater than the response to natural infection. Immune transfer to neonates occurred via placental and breastmilk.
    5. COVID-19 vaccine response in pregnant and lactating women: a cohort study
    1. 2021-03-23

    2. Covid Data Science. (2021) ‘UK Variant B.1.1.7 becoming the dominant strain in the USA - what does it mean for Spring/Summer?’. Accessed 26 March 2021. https://www.covid-datascience.com/post/uk-variant-b-1-1-7-becoming-the-dominant-strain-in-the-usa-what-does-it-mean-for-spring-summer

    3. Key points:The UK variant B.1.1.7 has become the dominant strain in the USA, going from 0.1% prevalence on January 1 to up near 50% prevalence by March 23.Bad News: This variant has been shown to produce higher viral loads, leading to ~50% higher transmission rates and ~50% higher death rates than wild type variantsGood News: Fortunately, this variant does not have "immune escape" capabilities, so previously infected and vaccinated individuals should retain their full protection.The increasing prevalence of this variant could produce a spring surge of cases, but this potential surge is partially mitigated by the following factors:An estimated 30-40% of USA residents have been exposed and infected with the virus and recent studies suggest a vast majority retain protection vs. reinfection for at least 6 months, and this protection should confer to this variant.A total of 25% of USA residents have received at least 1 dose of vaccine and 15% fully vaccinated, with 2.5 million/day currently vaccinated and suggesting that another 5% are vaccinated every week, with 60% fully vaccinated, and 80% receiving at least one dose, by the end of May, and the vaccine protection should confer to this variant.Between these factors, it might be 40-55% of USA residents have some protection vs. the UK B.1.1.7 and this % will continue to increase as vaccinations are done.However, given that >1/2 of USA residents are still currently susceptible to this variant, it is important to remain vigilant to do the small things to limit spread until a higher proportion are vaccinated to prevent a spring uptick that could threaten school and business openings.With the current pace of vaccination and pending durable immunity, population level protection vs. the wild type and UK B.1.1.7 variants may be widespread enough to prevent any further exponential spread by the time we reach the summertime. However, the "immune escape" variants like the South African B.1.351, Brazilian P.1, and New York B.1.526 could become more dominant in the summertime and produce more upticks in viral cases, but since it appears that the current vaccines maintain at least 2/3 efficacy vs. these escape variants, plus boosters are being developed that could restore full efficacy. Thus, the alarm and fear conveyed by some about these "immune escape" variants may be overblown, and it is still reasonable to expect that with broad vaccination we can have a summer and fall that more closely resembles normalcy.
    4. UK Variant B.1.1.7 becoming the dominant strain in the USA -- what does it mean for Spring/Summer?
    1. 2021-03-26

    2. de Oliveira T, Lutucuta S, Nkengasong J, Morais J, Paixao JP, Neto Z, Afonso P, Miranda J, David K, Ingles L, Amilton P A P R R C, Freitas H R, Mufinda F, Tessema K S , Tegally H, San E J, Wilkinson E, Giandhari J, Pillay S, Giovanetti M, Naidoo Y, Katzourakis A, Ghafari M, Singh L, Tshiabuila D, Martin D, Lessells R. (2021) A Novel Variant of Interest of SARS-CoV-2 with Multiple Spike Mutations Detected through Travel Surveillance in Africa. medRxiv. https://www.krisp.org.za/publications.php?pubid=330. Accessed 26 March 2021.

    3. At the end of 2020, the Network for Genomic Surveillance in South Africa (NGS-SA) detected a SARS-CoV-2 variant of concern (VOC) in South Africa (501Y.V2 or PANGO lineage B.1.351)1. 501Y.V2 is associated with increased transmissibility and resistance to neutralizing antibodies elicited by natural infection and vaccination2,3. 501Y.V2 has since spread to over 50 countries around the world and has contributed to a significant resurgence of the epidemic in southern Africa. In order to rapidly characterize the spread of this and other emerging VOCs and variants of interest (VOIs), NGS-SA partnered with the Africa Centres for Disease Control and Prevention and the African Society of Laboratory Medicine through the Africa Pathogen Genomics Initiative to strengthen SARS-CoV-2 genomic surveillance across the region. Here, we report the first genomic surveillance results from Angola, which has had 21 500 reported cases and around 500 deaths from COVID-19 up to March 2021 (Supplemental Fig S1). On 15 January 2021, in response to the international spread of VOCs, the government instituted compulsory rapid antigen testing of all passengers arriving at the main international airport, in addition to the existing requirement to present a negative PCR test taken within 72 hours of travel. All individuals with a positive antigen test are isolated in a government facility for a minimum of 14 days and require two negative RT-PCR tests at least 48 hours apart for de-isolation, whilst all travelers with a negative test on arrival proceed to mandatory self-quarantine for 10 days followed by a repeat test. In March 2021, we received 118 nasopharyngeal swab samples collected between June 2020 and February 2021, a number of which were from incoming air travelers (Supplemental Fig S1). From these, we produced 73 high quality genomes (>80% coverage), 14 of which were known VOCs/VOIs (seven 501Y.V2/B.1.351, six B.1.1.7, one B.1.525), 44 of which were C.16 (a common lineage circulating in Portugal), and twelve of which were other lineages (Supplemental Fig S2). In addition, we detected a new VOI in three incoming travelers from Tanzania who were tested together at the airport in mid-February. The three genomes from these passengers were almost identical and presented highly divergent sequences within the A lineage (Figure 1A & 1B). The GISAID database contains nine other sequences reported to be sampled from cases involving travel from Tanzania, two of which are basal to the three sampled in Angola This new VOI, temporarily designated A.VOI.V2, has 31 amino acid substitutions (11 in spike) and three deletions (all in spike) (Figure 1C & 1D). The spike mutations include three substitutions in the receptor-binding domain (R346K, T478R and E484K); five substitutions and three deletions in the N-terminal domain, some of which are within the antigenic supersite (Y144?, R246M, SYL247-249? and W258L)4; and two substitutions adjacent to the S1/S2 cleavage site (H655Y and P681H). Several of these mutations are present in other VOCs/VOIs and are evolving under positive selection.
    4. A novel variant of interest of SARS-CoV-2 with multiple spike mutations detected through travel surveillance in Africa
    1. 2021-03-24

    2. Hale, Thomas. ‘What We Learned from Tracking Every COVID Policy in the World’. The Conversation. Accessed 26 March 2021. http://theconversation.com/what-we-learned-from-tracking-every-covid-policy-in-the-world-157721.

    3. In March 2020, as COVID-19 swept around the globe, my colleagues and I began debating the bewildering new measures popping up around the world with our master’s students in a politics of policymaking class at the Blavatnik School of Government at Oxford University. We had a lot of questions. Why were governments doing different things? Which policies would work? We didn’t know. And to answer those questions, we needed comparable information on these new policies, including school closings, stay at home orders, contact tracing and more. A few weeks later, we launched the Oxford COVID-19 Government Response Tracker to help find these answers. It has now become the largest repository of global evidence relating to pandemic policies.
    4. What we learned from tracking every COVID policy in the world
    1. 2021-03-25

    2. Volz, E., Mishra, S., Chand, M. et al. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature (2021). https://doi.org/10.1038/s41586-021-03470-x

    3. The SARS-CoV-2 lineage B.1.1.7, designated a Variant of Concern 202012/01 (VOC) by Public Health England1, originated in the UK in late Summer to early Autumn 20202. Whole genome SARS-CoV-2 sequence data collected from community-based diagnostic testing shows an unprecedentedly rapid expansion of the B.1.1.7 lineage during Autumn 2020, suggesting a selective advantage. We find that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S-gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that the VOC has higher transmissibility than non-VOC lineages, even if the VOC has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with a larger share of under 20 year olds among reported VOC than non-VOC cases. Time-varying reproduction numbers for the VOC and cocirculating lineages were estimated using SGTF and genomic data. The best supported models did not indicate a substantial difference in VOC transmissibility among different age groups. There is a consensus among all analyses that the VOC has a substantial transmission advantage with a 50% to 100% higher reproduction number.
    4. 10.1038/s41586-021-03470-x
    5. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England
    1. Su, I., & Ceci, S. (2021, March 5). “Zoom Developmentalists”: Home-Based Videoconferencing Developmental Research during COVID-19. https://doi.org/10.31234/osf.io/nvdy6

    2. 2021-03-05

    3. 10.31234/osf.io/nvdy6
    4. As home-based video conferencing has become increasingly popular among developmental researchers during the COVID-19 pandemic, there is a pressing need to discuss its potentials and challenges. We have augmented our own experiences with insights from many “Zoom developmentalists” (see Acknowledgments) to provide recommendations for those who are considering engaging in home-based videoconferencing studies.
    5. “Zoom Developmentalists”: Home-Based Videoconferencing Developmental Research during COVID-19
    1. Kartushina, N., Mani, N., AKTAN-ERCIYES, A., Alaslani, K., Aldrich, N. J., Almohammadi, A., … Mayor, J. (2021, March 5). COVID-19 first lockdown as a unique window into language acquisition: What you do (with your child) matters. https://doi.org/10.31234/osf.io/5ejwu

    2. 2021-03-05

    3. 10.31234/osf.io/5ejwu
    4. The COVID-19 pandemic, and the resulting closure of daycare centers worldwide, led to unprecedented changes in children’s learning environments. This period of increased time at home with caregivers, with limited access to external sources (e.g., daycares) provides a unique opportunity to examine the associations between the caregiver-child activities and children’s language development. The vocabularies of 1742 children aged 8-36 months across 13 countries and 12 languages were evaluated at the beginning and end of the first lockdown period in their respective countries (from March to September 2020). Children who had less passive screen exposure and whose caregivers read more to them showed larger gains in vocabulary development during lockdown, after controlling for SES and other caregiver-child activities. Children also gained more words than expected (based on normative data) during lockdown; either caregivers were more aware of their child’s development or vocabulary development benefited from intense caregiver-child interaction during lockdown.
    5. COVID-19 first lockdown as a unique window into language acquisition: What you do (with your child) matters.
    1. Rodman, A. M., Rosen, M. L., Kasparek, S. W., Mayes, M., Lengua, L., McLaughlin, K. A., PhD, & Meltzoff, A. N. (2021, March 4). Social behavior and youth psychopathology during the COVID-19 pandemic: A longitudinal study. https://doi.org/10.31234/osf.io/y8zvg

    2. 2021-03-04

    3. 10.31234/osf.io/y8zvg
    4. Objective: The COVID-19 pandemic has brought unprecedented changes to the lives of youth, including social distancing measures and stay-at-home orders resulting in a sudden and stark reduction in daily social interactions for children and adolescents. Given that peer relationships are especially important during this developmental stage, it is crucial to understand the impact of COVID-19 pandemic on social behavior and risk for mental health problems in children and adolescents. Method: In a longitudinal sample (N=224, aged 7-15 years old) assessed at three strategic time points (prior to the pandemic, during the stay-at-home order period, and again six months later), we examine the social lives of children and adolescents and whether certain social behaviors may protect against increases in internalizing and externalizing problems during the pandemic. Results: Youth who reported lower levels of in-person and digital socialization, greater social isolation, and less peer and parent support had heightened internalizing and externalizing symptoms during the pandemic, controlling for pre-pandemic symptoms. Youth who reported more social connectedness and increased use of digital socialization during the pandemic were less likely to develop psychopathology after experiencing pandemic-related stressors. In addition, children, but not adolescents, who maintained some socialization in-person were less likely to develop internalizing symptoms following exposure to pandemic-related stressors. Conclusion: Using a longitudinal design, we identify social factors that promote well-being and resilience in children and adolescents during this societal event. We provide practical recommendations to mitigate risk of psychopathology resulting from the dramatic changes in youths’ social experiences during the pandemic.
    5. Social behavior and youth psychopathology during the COVID-19 pandemic: A longitudinal study
    1. Chevallier, Coralie, Anne-Sophie Hacquin, and Hugo Mercier. ‘COVID-19 Vaccine Hesitancy: Shortening the Last Mile’. PsyArXiv, 3 March 2021. https://doi.org/10.31234/osf.io/xchj6.

    2. 2021-03-06

    3. 10.31234/osf.io/xchj6
    4. We offer three recommendations to increase COVID-19 vaccination rates. First, use communication campaigns leveraging evidence-based levers and argumentation tools with experts. Second, use behavioral insights to make vaccination more accessible. Third, help early adopters communicate about their decision to be vaccinated to accelerate the emergence of pro-vaccination norms.
    5. COVID-19 vaccine hesitancy: shortening the last mile
    1. Heeren, A., HANSEEUW, B., Cougnon, L., & Lits, G. (2021, March 11). Excessive Worrying as the Driving Force of Anxiety During the First COVID-19 Lockdown-Phase in Belgium. https://doi.org/10.31234/osf.io/b34aj

    2. 2021-03-11

    3. 10.31234/osf.io/b34aj
    4. Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model (GGM) and a directed acyclic graph (DAG). Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both GGM and DAG pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to elucidate, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.
    5. Excessive Worrying as the Driving Force of Anxiety During the First COVID-19 Lockdown-Phase in Belgium
    1. Kozlowski, Diego, Jennifer Dusdal, Jun Pang, and Andreas Zilian. ‘Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation’. ArXiv:2011.02887 [Physics], 5 November 2020. http://arxiv.org/abs/2011.02887.

    2. 2020-11-05

    3. 2011.02887
    4. Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded.
    5. Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation