Social integration and risk of mortality among African-Americans: the Jackson heart study
Bibliographic record
Abstract
Abstract Objective Evidence suggests that greater social integration is related to lower mortality rates. However, studies among African-Americans are limited. We examined whether higher social integration was associated with lower mortality in 5306 African-Americans from the Jackson Heart Study, who completed the Berkman-Syme Social Network Index in 2000–2004 and were followed until 2018. Methods We estimated hazard ratios (HR) of mortality by categories of the Social Network Index (i.e., high social isolation, moderate social isolation [reference group], moderate social integration, high social integration) using Cox proportional hazard models. Covariates included baseline sociodemographics, depressive symptoms, health conditions, and health behaviors. Results Compared with moderate isolation, moderate integration was associated with an 11% lower mortality rate (HR = 0.89, 95% confidence interval [CI] 0.77, 1.03), and high integration was associated with a 25% lower mortality rate (HR = 0.75, 95% CI 0.64, 0.87), controlling for sociodemographics and depressive symptoms; compared with moderate isolation, high isolation was related to a 34% higher mortality rate (HR = 1.34, 95% CI 1.00, 1.79). Further adjustment of potential mediators (health conditions and health behaviors) only slightly attenuated HRs (e.g., HRmoderate integration = 0.90, 95% CI 0.78, 1.05; HRhigh integration = 0.77, 95% CI 0.66, 0.89). Conclusion Social integration may be a psychosocial health asset with future work needed to identify biobehavioral processes underlying observed associations with mortality among African-Americans.
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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 | 0.003 |
| 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 | 0.001 |
| 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".