Gender inequity needs to be regarded as a social determinant of depressive symptoms: Results from the Northern Swedish cohort
Bibliographic record
Abstract
BACKGROUND: The importance of social and avoidable determinants of depressive symptoms has been increasingly recognized in public health research. However, when it comes to determinant of gender differences in depressive symptoms the focus is predominantly on biological unavoidable determinants. Thus, there is a need for more focus on gendered social determinants of health. The aim of this study was to analyse the importance of gender relations for depressive symptoms after taking socioeconomic factors and earlier depressive symptoms into account in the Northern Swedish cohort. METHODS: A 26-year follow-up study of a cohort of all school leavers in a middle-sized industrial town in Northern Sweden was performed from age 16 until age 42. Of those still alive of the original cohort, 94% participated during the whole period and answered extensive questionnaires. Exposure was measured as socioeconomic status, financial strain, perceived gender inequity in the couple relationship and division of responsibility for domestic work. The outcome was depressive symptoms at age 42, while depressive symptoms were controlled at age 30. RESULTS: In multivariate logistic regression analyses significant relations between financial strain and, among women only, also perceived gender equity in the couple relationship and depressive symptoms after adjustment for earlier health status, as well as for all other exposure measures. CONCLUSIONS: Financial strain, and among women, also gender inequity in the couple relationship was related to depressive mood. There is a need to pay more attention to gender relations in future research on social determinants of depressive mood.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".