Bibliographic record
Abstract
Background Research has shown network social capital associated with a range of health behaviours and conditions. Little is known about what social capital inequalities in health represent, and which social factors contribute to such inequalities. Methods Data come from the Montreal Neighbourhood Networks and Healthy Aging Study (n=2707). A position generator was used to collect network data on social capital. Health outcomes included self-reported health (SRH), physical inactivity, and hypertension. Social capital inequalities in low SRH, physical inactivity, and hypertension were decomposed into demographic, socioeconomic, network and psychosocial determinants. The percentage contributions of each in explaining health disparities were calculated. Results Across the three outcomes, higher educational attainment contributed most consistently to explaining social capital inequalities in low SRH ( % C=30.8%), physical inactivity (15.9%), and hypertension (51.2%). Social isolation, contributed to physical inactivity (11.7%) and hypertension (18.2%). Sense of control (24.9%) and perceived cohesion (11.5%) contributed to low SRH. Age reduced or increased social capital inequalities in hypertension depending on the age category. Conclusions Interventions that include strategies to reduce socioeconomic inequalities and increase actual and perceived social connectivity may be most successful in reducing social capital inequalities in health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.834 | 0.642 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".