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Record W1969696779 · doi:10.1017/s1744133108004556

Access to psycho-social resources and health: exploratory findings from a survey of the French population

2008· article· en· W1969696779 on OpenAlexaff
Florence Jusot, Michel Grignon, Paul Dourgnon

Bibliographic record

VenueHealth Economics Policy and Law · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster UniversityInstitute of Health Economics
Fundersnot available
KeywordsSocial engagementSocial capitalPsychologySocial positionAffect (linguistics)PopulationSocial psychologySocial mobilitySocial supportSocial inequalitySociologyInequalitySocial relationDemographySocial science

Abstract

fetched live from OpenAlex

We study the psycho-social determinants of self-assessed health in order to explain social inequalities in health in France. We use a unique general population survey to assess the respective impact on self-assessed health status of subjective perceptions of social capital, social support, and sense of control, controlling for standard socio-demographic factors (SES, income, education, age, and gender). The survey is unique in that it provides a variety of measures of self-perceived psycho-social resources (trust and civic engagement, social support, sense of control, and self-esteem). We find empirical support for the link between the subjective perception of psycho-social resources and health. Sense of control at work is the most important correlate of health status after income. Other important ones are civic engagement and social support. To a lesser extent, sense of being lower in the social hierarchy is associated with poorer health status. On the contrary, relative deprivation does not affect health in our survey. Since access to psycho-social resources is not equally distributed in the population, these findings suggest that psycho-social factors can partially explain of social inequalities in health in France.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.189
GPT teacher head0.418
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2008
Admission routes1
Has abstractyes

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