Relative Health Effects of Education, Socioeconomic Status and Domestic Gender Inequity in Sweden: A Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Limited existing research on gender inequities suggests that for men workplace atmosphere shapes wellbeing while women are less susceptible to socioeconomic or work status but vulnerable to home inequities. METHODS: Using the 2007 Northern Swedish Cohort (n = 773) we identified relative contributions of perceived gender inequities in relationships, financial strain, and education to self-reported health to determine whether controlling for sex, examining interactions between sex and other social variables, or sex-disaggregating data yielded most information about sex differences. RESULTS AND DISCUSSION: Men had lower education but also less financial strain, and experienced less gender inequity. Overall, low education and financial strain detracted from health. However, sex-disaggregated data showed this to be true for women, whereas for men only gender inequity at home affected health. In the relatively egalitarian Swedish environment where women more readily enter all work arenas and men often provide parenting, traditional primacy of the home environment (for women) and the work environment (for men) in shaping health is reversing such that perceived domestic gender inequity has a significant health impact on men, while for women only education and financial strain are contributory. These outcomes were identified only when data were sex-disaggregated.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".