Psychological distress inequality between employed men and women: A gendered exposure model
Bibliographic record
Abstract
This study examines an exposure model in which the work and family stressors and the access to resources are gendered and contribute to explaining the psychological distress inequality between sex categories, both directly and indirectly through work-family conflict. A multilevel path analysis conducted on a random cross-sectional sample of 2026 Canadians workers from 63 establishments was performed. Our exposure model fully explains the higher level of psychological distress among working women compared to working men. Women are more exposed to work-to-family conflict, have less decision authority, are more likely to be a single parent and have less self-esteem, factors that are directly associated with a higher level of psychological distress. On the other hand, women work fewer hours, have less irregular or evening schedules and have more social resources outside of work, which contribute to lower their level of psychological distress through less work-to-family conflict. By identifying which of the differences in exposure to work and family stressors and resources explain the greater psychological distress of working women compared to working men, and by examining the mediating role of work-family conflict in this process, this study identified specific paths to reduce psychological distress inequality between women and men in the workplace.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".