Stressful life events and depressive symptoms during COVID‐19: A gender comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it