Capital interplays and the self-rated health of young men: results from a cross-sectional study in Switzerland
Bibliographic record
Abstract
INTRODUCTION: We apply capital interplay theory to health inequalities in Switzerland by investigating the interconnected effects of parental cultural, economic and social capitals and personal educational stream on the self-rated health of young Swiss men who live with their parents. METHODS: We apply logistic regression modelling to self-rated health in original cross-sectional survey data collected during mandatory conscription of Swiss male citizens in 2010 and 2011 (n = 23,975). RESULTS: In comparison with sons whose parents completed mandatory schooling only, sons with parents who completed technical college or university were significantly more likely to report very good or excellent self-rated health. Parental economic capital was an important mediating factor in this regard. Number of books in the home (parental cultural capital), family economic circumstances (parental economic capital) and parental ties to influential people (parental social capital) were also independently associated with the self-rated health of the sons. Although sons in the highest educational stream tended to report better health than those in the lowest, we found little evidence for a health-producing intergenerational transmission of capitals via the education stream of the sons. Finally, the positive association between personal education and self-rated health was stronger among sons with relatively poorly educated parents and stronger among sons with parents who were relatively low in social capital. CONCLUSIONS: Our study provides empirical support for the role of capital interplays, social processes in which capitals interpenetrate or co-constitute one another, in the intergenerational production of the health of young men in Switzerland.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".