Stressful life events and depressive symptoms during COVID‐19: A gender comparison
Bibliographic record
Abstract
The COVID-19 pandemic precipitated a wide range of public health, economic, social, and political shocks, setting in motion life events that reverberated to affect individuals' mental health. Moving beyond a checklist approach, this study drew on individuals' own words to identify both conventional and novel sources of stress during COVID-19 and examine the role of stressful life events in producing gender disparities in depressive symptoms. Drawing on a 2021 U.S. nationally representative survey, we coded text responses to an open-ended question on stressful life events and conducted descriptive and regression analyses (n = 1733). The analyses revealed three key findings. First, men were more likely to report having experienced no stressful life events or else mention politics as a source of stress. Women, by comparison, were more likely to report the following as stressful-inability to socialize, paid work, care work, health, or the death of loved ones. Second, for both women and men, respondents reporting no stressful life events had the lowest, and those reporting finances as the most stressful life event had the highest, depressive symptoms. Third, women had higher depressive symptoms than men, and mediation analysis showed that stressful life events explained approximately a third of the gender gap in depressive symptoms. The findings indicate that policies attending to people's financial stress are important for mitigating mental health risks in turbulent times. Interventions that reduce women's exposure to stressful life events are also crucial to bridging gender disparities in mental health.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".