Social vulnerability increases the risk of death differently in men and women: longitudinal analysis over 15 years in the Paquid Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Social vulnerability (SV) is a multidimensional construct associated with adverse health outcomes, including mortality. However, little is known about how SV differentially affects older men and women. RESEARCH DESIGN AND METHODS: We analyzed data from 3695 community-dwelling older adults from the Paquid cohort, followed for 15 years. SV was assessed using a 26-item SV Index (SVI), categorized into low, moderate, and high levels. Delayed-entry Cox models stratified by gender were used to estimate mortality risk, adjusting for disability, history of ischemic heart disease, dyspnea, diabetes, and cognitive impairment. Associations between SV subdimensions and mortality were also examined separately by gender. RESULTS: Women accumulated more social deficits than men (40 % vs. 21 % with high SV). High SV was associated with a 21-25 % increased mortality risk in both genders. However, moderate SV is significantly associated with an increased mortality risk only in men (adjusted Hazard Ratio = 1.25, 95 % CI: 1.09-1.44 vs. aHR = 0.96, 95 % CI: 0.81-1.13 in women). Among subdimensions, low socioeconomic status and poor leisure activity engagement were the strongest mortality predictors in men-even at moderate levels (result marginally significant for leisure activities, p = 0.073). In women, poor engagement in leisure activities and negative psychological experience were the main predictors of mortality; low socioeconomic status (SES) showed a trend-level association (p = 0.045). DISCUSSION AND IMPLICATIONS: Despite greater SV, women seem to withstand moderate social deficits better than men. These findings highlight the need for further studies to explain gender differences and develop gender-sensitive public health interventions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".