A prospective study of individual-level social capital and major depression in the United States
Bibliographic record
Abstract
STUDY OBJECTIVE: To investigate prospectively the associations between depression and cognitive social capital (social trust, sense of belonging, mutual aid) and structural social capital (volunteer work and community participation). METHODS: This was a prospective study that was carried out in the USA. The participants were a nationally representative sample of 724 English-speaking non-institutionalised adults (25-74 years old) who participated in the National Survey of Midlife Development in the United States (MIDUS) in 1995-6 and the MIDUS Psychological Experience Follow-Up study in 1998. MAIN RESULTS: In multivariable adjusted logistic regression analyses, those who trusted their neighbours were less likely to develop major depression (MD) during follow-up than those who reported low levels of social capital on these dimensions (adjusted OR of MD for high vs low trust = 0.43; 95% CI 0.20 to 0.93, adjusted for MD at baseline, age, gender, race, education, working status, marital status, physical health and extroversion traits). Structural dimensions of social capital were not associated with MD in adjusted models. CONCLUSIONS: Perceptions of higher levels of cognitive social capital (trust of neighbours) are associated with lower risks of developing MD during 2-3 year follow-up. However, after excluding participants with MD at the baseline, the association between trust and MD became non-significant. Structural dimensions were not associated with MD.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".