Cross-Sectional and Longitudinal Association between Sleep and HIV Prevention and Care Behaviors Among Transgender Women of Color: The TURNNT Cohort Study
Bibliographic record
Abstract
Background: Transgender women of color have shorter and worse-quality sleep, putting them at risk for worse health outcomes. We investigated the causal effect of sleep on HIV care and prevention outcomes among transgender women of color.Methods: We used data from transgender women of color living in New York City from the Trying to Understand Relationships, Network and Neighborhoods Among Transgender Women of Color (TURNNT) Cohort Study. Our exposures of interest were short sleep (sleeping for less than seven hours per night), poor-quality sleep (self-rated quality of sleep as “very bad” or “fairly bad”), and sleep onset latency (taking at least 30 minutes to fall asleep). We asked participants about their HIV care and prevention outcomes, including HIV/STI testing, condom use, PrEP use, and HIV viral load suppression. We used Targeted Maximum Likelihood Estimation (TMLE) to estimate the causal relative risk (RR) of sleep on these outcomes and included age, education, income, US-born nativity, and hormone replacement therapy use as potential confounders.Findings: Among the 314 participants, 54·5% had short sleep, 35·0% had poor-quality sleep, and 55·4% experienced sleep onset latency. Worse sleep health had positive and negative effects on HIV outcomes. Among people living with HIV (46·2%), those who had short amounts of sleep were less likely to always use a condom (RR 0·67, 95% CI 0·51–0·87]), and those who had poor-quality sleep were less likely to be virally suppressed (0·83, 0·72–0·96]). However, sleep onset latency was positively predictive of STI testing (1·19, 1·03–1·37]). Among people not living with HIV (51·6%), experiencing sleep onset latency decreased the likelihood of always condom use (0·46, 0·28–0·74).Interpretation: Improving sleep health among transgender women of color could improve HIV care and prevention outcomes and reduce health inequities. More research is needed to understand how sleep health affects HIV care and prevention 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".