Depression, Sex and Gender Roles in Older Adult Populations: The International Mobility in Aging Study (IMIAS)
Bibliographic record
Abstract
OBJECTIVES: To assess the associations between gender roles and depression in older men and women and whether gender roles are independent risk factors for depression. METHODS: International cross-sectional study of adults between 65 and 74 years old (n = 1,967). Depression was defined by a score of 16 or over in the Center for Epidemiologic Studies Depression Scale (CES-D). A validated 12-item Bem Sex Role Inventory (BSRI) was used to classify participants in gender roles (Masculine, Feminine, Androgynous, and Undifferentiated) using research site medians of femininity and masculinity as cut-off points. Poisson regressions were fitted to estimate the prevalence ratios (PR) of depression for each gender role compared to the masculine role, adjusting for sex, sufficiency of income, education, marital status, self-rated health, and chronic conditions. RESULTS: Among men, 31.2% were androgynous, 26% were masculine, 14.4% were feminine, and 28.4% were undifferentiated; among women, the corresponding percentages were 32.7%, 14.9%, 27%, and 25.4%. Both in men and in women, depressive symptoms (CES-D≥16) were more prevalent in those endorsing the undifferentiated type, compared to masculine, feminine or androgynous groups. However, after adjusting for potential confounders, compared to the masculine group only those endorsing the androgynous role were 28% less likely to suffer from depression: PR of 0.72 (95% CI: 0.55-0.93). In fully adjusted models, prevalence rates of depression were not different from masculine participants in the two other gender groups of feminine and undifferentiated. CONCLUSIONS: Androgynous roles were associated with lower rates of depression in older adults, independently of being a man or a woman.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".