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Record W3021395141 · doi:10.1101/2020.05.05.20091058

Emotional consequences of COVID-19 home confinement: The ECLB-COVID19 multicenter study

2020· preprint· en· W3021395141 on OpenAlexaff
Achraf Ammar, Patrick J. Mueller, Khaled Trabelsi, Hamdi Chtourou, Omar Boukhris, Liwa Masmoudi, Bassem Bouaziz, Michael Brach, Marlen Schmicker, Ellen Bentlage, Daniella How, Mona Ahmed, Asma Aloui, Omar Hammouda, Laisa Liane Paineiras-Domingos, Annemarie Braakman‐Jansen, Christian Wrede, Sophia Bastoni, Carlos Soares Pernambuco, Leonardo José Mataruna-Dos-Santos, Morteza Taheri, Khadijeh Irandoust, Aïmen Khacharem, Nicola Luigi Bragazzi, Karim Chamari, Jordan M. Glenn, Nicholas T. Bott, Faı̈ez Gargouri, Lotfi Chaâri, Hadj Batatia, Gamal Mohamed Ali, Osama Abdelkarim, Mohamed Jarraya, Kaïs El Abed, Nizar Souissi, Stephen J. Bailey, Wassim Moalla, Jonathan Gómez‐Raja, Monique Epstein, Robbert Sanderman, Sebastian Viktor Waldemar Schulz, Achim Jerg, Ramzi Al-Horani, Taiysir Mansi, Mohamed Jmail, Fernando Barbosa, Fernando Ribeiro dos Santos, Boštjan Šimunič, Rado Pišot, Donald Cowan, Andrea Gaggioli, Jürgen M. Steinacker, Laurel Riemann, Bryan L. Riemann, Notger Mueller, Tarak Driss, Anita Höekelmann

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMental healthSocial distanceMoodFeelingPsychologyGovernment (linguistics)Coronavirus disease 2019 (COVID-19)Computer-assisted web interviewingClinical psychologyMedicinePsychiatrySocial psychologyBusiness

Abstract

fetched live from OpenAlex

Abstract Background Public health recommendations and government measures during the COVID-19 pandemic have enforced restrictions on daily living, which may include social distancing, remote work/school, and home confinement. While these measures are imperative to abate the spreading of COVID-19, the impact of these restrictions on mental health and emotional wellbeing is undefined. Therefore, an international online survey was launched on April 6, 2020 in seven languages to elucidate the impact of COVID-19 restrictions on mental health and emotional well-being. This report presents the preliminary results from the first thousand responders on mental wellbeing and mood and feelings questionnaires. Methods The ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists and academics, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform. Thirty-five research organizations from Europe, North-Africa, Western Asia and the Americas promoted the multi-languages survey through their networks to general society. Of the 64 questions, 7 were from the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS), and 13 were from the Short Mood and Feelings Questionnaire (SMFQ), which are both validated instruments. Results Analysis was conducted on the first 1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%). The COVID-19 home confinement had a negative effect on both mental wellbeing and on mood and feelings. Specifically, a significant decrease (p<0.001 and Δ%= 9.4 %) in the total score of mental wellbeing was noted. More individuals (+12.89%) reported a low mental wellbeing “during” compared to “before” home confinement. Furthermore, results from the mood and feelings questionnaire (i.e., depressive symptoms) showed a significant increase by 44.9% (p<0.001) in total score with more people (+10%) developing depressive symptoms “during” compared to “before” home confinement. Conclusion The ECLB-COVID19 survey revealed an increased psychosocial strain triggered by the enforced home confinement. To mitigate this high risk of mental disorders and to foster an Active and Healthy Confinement Lifestyle (AHCL), a crisis-oriented interdisciplinary intervention is urgently needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.151
GPT teacher head0.438
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2020
Admission routes1
Has abstractyes

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