Differences between women’s and men’s socioeconomic inequalities in health: longitudinal analysis of the Canadian population, 1994–2003
Bibliographic record
Abstract
BACKGROUND: Socioeconomic inequalities in health are ubiquitous in developed countries; however, whether these inequalities differ between women and men over time is less clear. OBJECTIVE: To estimate the potentially different health effects of changes in socioeconomic position (SEP) on changes in health for working-age women and men over a 10-year period. Three main questions were addressed: (1) are there health differences between women and men over time, (2) do changes in SEP lead to health inequalities and (3) do changes in SEP impact health differently for women and men? METHODS: Generalised estimating equations models were used to analyse cycles 1-5 of the Canadian National Population Health Survey for four measures of health, number of chronic conditions, self-rated health, functional health and mental distress, and three measures of SEP, income, education and employment status. RESULTS: Health inequalities by sex/gender and by changes in SEP were present for all four outcomes in age-adjusted models; however, after controlling for time-dependent social structure, behaviour, and psychosocial factors the relationships persisted only for chronic conditions and psychological distress. There was no evidence that these effects differed, over time, between women and men. CONCLUSIONS: Men and women in this nationally representative sample of Canadians do not differentially embody changes in SEP, although both sex/gender and changes in SEP independently impact 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".