The role of psychosocial factors in explaining sex differences in major depression and generalized anxiety during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Understanding how pandemics differentially impact on the socio-protective and psychological outcomes of males and females is important to develop more equitable public health policies. We assessed whether males and females differed on measures of major depression and generalized anxiety during the COVID-19 the pandemic, and if so, which sociodemographic, pandemic, and psychological variables may affect sex differences in depression and anxiety. Methods Participants were a nationally representative sample of Irish adults (N = 1,032) assessed between April 30thto May 19th, 2020, during Ireland’s first COVID-19 nationwide quarantine. Participants completed self-report measures of anxiety (GAD-7) and depression (PHQ-9), as well as 23 sociodemographic pandemic-related, and psychological variables. Sex differences on measures of depression and anxiety were assessed using binary logistic regression analysis and differences in sociodemographic, pandemic, and psychological variables assessed using chi-square tests of independence and independent samples t-tests. Results Females were significantly more likely than males to screen positive for major depressive disorder (30.6% vs. 20.7%;χ2(1) = 13.26,p < .001, OR = 1.69 [95% CI = 1.27, 2.25]), and generalised anxiety disorder (23.3% vs. 14.4%;χ2(1) = 13.42,p < .001, OR = 1.81 [95% CI = 1.31, 2.49]). When adjusted for all other sex-varying covariates however, sex was no longer significantly associated with screening positive for depression (AOR = 0.80, 95% CI = 0.51, 1.25) or GAD (AOR = 0.97, 95% CI = 0.60, 1.57). Conclusion Observed sex-differences in depression and anxiety during the COVID-19 pandemic in the Republic of Ireland are best explained by psychosocial factors of COVID-19 related anxiety, trait neuroticism, lower sleep quality, higher levels of loneliness, greater somatic problems, and, in the case of depression, increases in childcaring responsibilities and lower trait consciousnesses. Implications of these findings for public health policy and interventions are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".