Looking for capacities rather than vulnerabilities: The moderating effect of health assets on the associations between adverse social position and health
Bibliographic record
Abstract
To increase capacities and control over health, it is necessary to foster assets (i.e. factors enhancing abilities of individuals or communities). Acting as a buffer, assets build foundations for overcoming adverse conditions and improving health. However, little is known about the distribution of assets and their associations with social position and health. In this study, we documented the distribution of health assets and examined whether these assets moderate associations between adverse social position and self-reported health. A representative population-based cross-sectional survey of adults in the Eastern Townships, Quebec, Canada (n = 8737) was conducted in 2014. Measures included assets (i.e. resilience, sense of community belonging, positive mental health, social participation), self-reported health (i.e. perceived health, psychological distress), and indicators of social position. Distribution of assets was studied in relation to gender and social position. Logistic regressions examined whether each asset moderated associations between adverse social position and self-reported health. Different distributions of assets were observed with different social positions. Women were more likely to participate in social activities while men were more resilient. Resilience and social participation were moderators of associations between adverse social position (i.e. living alone, lower household income) and self-reported health. Having assets contributes to better health by increasing capacities. Interventions that foster assets and complement current public health services are needed, especially for people in unfavorable situations. Health and social services decision-makers and practitioners could use these findings to increase capacities and resources rather than focusing primarily on preventing diseases and reducing risk factors.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".