Women suffer but men die: survey data exploring whether this self-reported health paradox is real or an artefact of gender stereotypes
Bibliographic record
Abstract
BACKGROUND: Despite consistently reporting poorer health, women universally outlive men. We examine whether gender differences in lived circumstances considered, and meaning attributed to SRH by women and men might explain this paradox. METHODS: In an online survey 917 adults rated their health (SRH) and mental health (SRMH) and reflected upon what life experiences they considered in making their ratings. Descriptive findings were sex-disaggregated. The multiple experiences listed were then subject to factor analyses using principal components methods and orthogonal rotation. RESULTS: Women reported poorer SRH and SRMH. They considered a wider range of circumstances, weighing all but self-confidence and behaviors as more important to SRH than did men. Two underlying components, psychosocial context and clinical status were identified overall. Physical health and pain were more important elements of men's clinical status and behaviors. Comparisons with others of the same age played a larger role in male psycho-social context. Two components also underpinned SRMH. These were clinical problems and psycho-social circumstances for which self-confidence was only important among men. CONCLUSIONS: Women's and men's common interpretation of measures like SRH suggests that women's health disadvantage is neither artefactual nor determined by gendered meanings of measures and does not explain the paradox. SRH and SRMH captured social circumstances for all. Convergence of characteristics women and men consider as central to health is evidence of the dynamism of gender with evolving social norms. The remaining divergence speaks to persisting traditional male stereotypes.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".