Clase social, desigualdades en salud y conductas relacionadas con la salud de la población trabajadora en Chile
Bibliographic record
Abstract
OBJECTIVE: To analyze links between social class and health-related indicators and behaviors in Chilean workers, from a neo-Marxian perspective. METHODS: A cross-sectional study based on the First National Survey on Employment, Work, Health, and Quality of Life of Workers in Chile, done in 2009-2010 (n = 9 503). Dependent variables were self-perceived health status and mental health, examined using the General Health Questionnaire (GHQ-12). Health-related behavior variables included tobacco use and physical activity. The independent variable was neo-Marxian social class. Descriptive analyses of prevalence were performed and odds ratio (OR) models and 95% confidence intervals (95%CI) were estimated. RESULTS: Medium employers (between 2 and 10 employees) reported a lower prevalence of poor health (21.6% [OR 0.68; 95%CI 0.46-0.99]). Unskilled managers had the lowest mental health risk (OR 0.43; 95%CI 0.21-0.88), with differences between men and women. Large employers (more than 10 employees) reported smoking the least, while large employers, expert supervisors, and semi-skilled workers engaged in significantly more physical activity. CONCLUSIONS: Large employers and expert managers have the best health-related indicators and behaviors. Formal proletarians, informal proletarians, and unskilled supervisors, however, have the worst general health indicators, confirming that social class is a key determinant in the generation of population health inequalities.
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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 | 0.001 | 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".