Incarceration, Social Support Networks, and Health among Black Sexual Minority Men and Transgender Women: Evidence from the HPTN 061 Study
Bibliographic record
Abstract
Support from social networks buffers against negative effects of stress but is disrupted by incarceration. Few studies examine incarceration, social support networks, and health among Black sexual minority men (BSMM) and Black transgender women (BTW). We conducted a secondary analysis using HIV Prevention Trials Network 061 (HPTN 061), a sample of BSMM/BTW recruited from six US cities. We measured associations between recent incarceration reported at six months follow-up and social support networks at twelve months follow-up, and cross-sectional associations between support networks and twelve-month health outcomes (e.g., sexual partnerships, substance use, healthcare access and depressive symptoms). Among the analytic sample (N = 1169), recent incarceration was associated with small medical support networks (adjusted risk ratio [aRR] 1.16, 95% CI 1.01, 1.34) and small financial support networks (aRR 1.18, 95% CI 1.04, 1.35). Support networks were associated with multiple partnerships (adjusted prevalence ratio [aPR] 0.77, 95% CI 0.65, 0.90), unhealthy alcohol use (aPR 1.20, 95% CI 0.96, 1.51), and depressive symptoms (aPR 1.16, 95% CI 0.99, 1.36). Incarceration adversely impacts social support networks of BSMM/BTW, and support networks were associated with a range of important health outcomes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".