Multiple jeopardy, national wealth and perceived discrimination: Subjective health of intersecting minority groups across 28 countries
Bibliographic record
Abstract
Objective: Belonging to social minority groups is detrimental for health outcomes, yet it is still unclear how multiple social minority statuses combine in their effect on health and whether perceived discrimination explains this link. Moreover, the moderating role of the societal context on the multiple social minority status-health link has never been tested. The current study employs a comprehensive conceptual framework to better understand the patterns of association between health outcomes and multiple social minority statuses. Methods and measures: Using data from the European Social Survey (N = 53,161 from 28 countries) and multi-level structural equation modelling, the study examines whether older age, female gender and ethnic minority status have additive, exacerbation or inurement effects on subjective health, whether perceived discrimination mediates these relations, and whether national wealth moderates the associations. Results: Old age and female gender, but not ethnicity, were related to adverse health outcomes, especially in poorer countries. Belonging to two, but not three, social minority groups exacerbated health outcomes. Perceived discrimination explained some of the (multiple) social minority status-health links, whereas an ethnicity-related health risk was fully mediated by perceived discrimination. Conclusion: Supporting the idea of intersectionality, different combinations of social minority statuses differ in health outcomes as well as the underlying mechanisms.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".