Work-family interface and mental health inequalities between women and men: A gendered exposure model across Canadian provinces
Bibliographic record
Abstract
Women face a higher risk of common mental health disorders than men, an association that has largely been attributed to their greater exposure to stressors. However, studies testing the exposure hypothesis among the employed population rarely take into account the work-family interface and neglect the macro-social context in the construction of gender. This study examines a gendered exposure model, stratified by Canadian province, in which differences between working women and men in work and family conditions, work-family conflict, and work-family enrichment are linked to self-reported mental health inequalities. Path analyses were conducted for Alberta, British Columbia, Quebec, and Ontario using data from 6,786 employed respondents to the 2022 Canadian Community Health Survey. The exposure hypothesis was tested through indirect associations between sex categories and mental health via work and family conditions and the work-family interface. Findings show that in Quebec, women report higher work stress, which is indirectly linked to poorer mental health through increased work-to-family conflict. In Alberta, women report more work stress, which is associated with poorer mental health. Women also work fewer hours than men, a factor linked to poorer mental health in Quebec and Ontario. Overall, the results indicate that work-family stressors and resources contribute more to provincial differences in mental health than in gendered mental health inequalities, highlighting the need to differentiate between general determinants of mental health and the factors driving mental health disparities. This study emphasizes the importance of integrating the work-family interface when documenting the structuring influence of gender on mental health inequalities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".