Association of individual network social capital with abdominal adiposity, overweight and obesity
Bibliographic record
Abstract
BACKGROUND: Limited research has examined the association of individual trust, participation and social capital with obesity using objective measures of waist circumference (WC), body mass index (BMI) and network measures of social capital. METHODS: Data were obtained from a representative sample of Montreal residents. Participants completed questionnaires that included a position generator for collecting network social capital data. Measures of WC, height and weight were collected by registered nurses. To estimate associations with cardiometabolic risk, data on WC for individuals with BMI between 18.5 and 34.9 were extracted for analysis (n = 291). Using a proportional odds model with clustered robust standard errors, we evaluated the association of three different measures of individual social capital with elevated and substantially elevated WC and overweight and obesity categories of BMI. These measures were then evaluated in their associations with elevated WC and BMI, adjusting for socio-demographic and behavioral covariates. RESULTS: Network social capital was inversely associated with the likelihood of being in an elevated WC risk category (odds ratio (OR) = 0.81, 95% confidence intervals (CI: 0.69, 0.96) and higher BMI category (OR = 0.81, 95% CI: 0.71, 0.92). CONCLUSION: Higher individual network social capital is associated with a lower likelihood of elevated WC risk and overweight and obesity.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".