[Social capital and mental health in representative sample od chilean workers].
Bibliographic record
Abstract
UNLABELLED: Antecedents. The social capital has been considered an important factor through which the effect of the macro structural social causes for physical and mental health of the people could be partly understood. However, the studies in Latin America are limited. OBJECTIVE: Determine the possible associations of the dimensions of social capital with the level of psychological distress in the workers. MATERIALS AND METHODS: The sample included 1557 workers from all economic areas. 65% were men, with an average age of 37,0 and 14,4 years of schooling. A survey was used and it was adapted and validated for this job. The psychological distress was measured with GHQ-12. RESULTS: The social capital survey showed good psychometric characteristics and it included three factors (trust, reciprocity, organization and community participation). Significant differences were shown for the level of trust and reciprocity by gender, age and years of schooling. In general, a correlation which was statistically significant was found between the level of psychological distress and the three dimensions of the social capital, especially in the subgroups of women of an intermediate age (41 to 60 years) who have attended primary school (9 to 12 years of schooling). CONCLUSION: The social capital survey showed good psychometric properties which could be useful for other population studies. The association among the three dimensions of social capital with mental health was confirmed, specially in some subgroups. The development of social capital in our countries should be the motive behind public policies and regular evaluations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".