Sex and Gender Impact Mental and Emotional Well-Being During COVID-19 Pandemic: A European Countries Experience
Bibliographic record
Abstract
Objective: The COVID-19 pandemic influences mental health drastically. Therefore, our aim was to investigate whether biological sex and gender-related factors are associated with mental health during the COVID-19 pandemic. Materials and Methods: The International COVID-19 Awareness and Responses Evaluation Study is an international multiwave cross-sectional observational cohort study of public awareness, attitudes, and responses to public health policies (www.mbmc-cmcm.ca/covid19). The study is led by the Montreal Behavioural Medicine Centre in collaboration with 200 international collaborators from 42 countries. It has received research ethics board approval from the Comité d'éthique de recherche du CIUSSS-NIM (Centre intégré universitaire de santé et de services sociaux du Nord- de-l'île-de-Montréal), approval no.: 2020-2099/25-03-2020. Recruitment began on March 27, 2020, and the survey is available in 34 languages. The associations between biological sex, sociocultural gender, and mental health were assessed in multivariate logistic regression models only for the European population (n = 12,300). Results: Positive correlations were found between female sex and “feeling nervous, anxious or worried” (OR = 3.2, p < 0.001, 95% CI 1.87–5.63) and “feeling sad, depressed or hopeless” (OR 1.8, p = 0.031, CI 1.05–3.05). Male sex was related to more frequently “feeling irritable, frustrated, and angry” (OR = 1.8, p = 0.04, 95% CI 1.03–2.99). Concerning gender, a negative correlation between being employed and “feeling lonely or isolated” (OR = 0.26, p < 0.001, 95% CI 0.11–0.59) was observed in the female cohort. Conclusion: Sex and gender differences exist in the emotional responses during the COVID-19 pandemic. Especially, within the female cohort, unemployment is negatively associated with mental health. Therefore, this study suggests more targeted psychological and social support for females during the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".