Psychosocial Resources and Health Inequalities in France
Bibliographic record
Abstract
This study addresses the issue of psychosocial determinants of health, i.e. non material resources such as social capital, social support, and sense of control or self-esteem. We firstly analyse their impact on health status in addition to material and biological determinants. We then measure the social differences in access to these resources. Lastly, put aside psychosocial factors can partly explain social inequalities in health in France. Whereas the impact of psychosocial resources on health is most often described from a contextual perspective, considering that a higher level of resources in the area improves health on average for individuals living in the area, psychosocial resources are here assessed at the individual level to take into account the actual or perceived access of individuals to the level of resources. We use a general population survey, representative of employed individuals aged 16 to 64 living in France in 2004, to study the association between health status and the subjective perception of social capital, social support, sense of control and self-esteem, controlling for standard socio-demographic factors (occupation, income, education, age and gender). Several health outcomes are considered: self-assessed health, long term activity limitations, main chronic conditions, obesity, tobacco consumption, and chronic excessive alcohol consumption. We find empirical support for the link between the subjective perception of psychosocial resources and health or health-related behaviours. More specifically health status is positively associated with access to social capital, emotional support and sense of control at work. Since access to psychosocial resources is not equally distributed in the population, these findings suggest that psychosocial factors can partly explain social inequalities in health in France.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".