Longitudinal cohort of HIV-negative transgender women of colour in New York City: protocol for the TURNNT (‘Trying to Understand Relationships, Networks and Neighbourhoods among Transgender women of colour’) study
Bibliographic record
Abstract
INTRODUCTION: In the USA, transgender women are among the most vulnerable to HIV. In particular, transgender women of colour face high rates of infection and low uptake of important HIV prevention tools, including pre-exposure prophylaxis (PrEP). This paper describes the design, sampling methods, data collection and analyses of the TURNNT ('Trying to Understand Relationships, Networks and Neighbourhoods among Transgender women of colour') study. In collaboration with communities of transgender women of colour, TURNNT aims to explore the complex social and environmental (ie, neighbourhood) structures that affect HIV prevention and other aspects of health in order to identify avenues for intervention. METHODS AND ANALYSES: TURNNT is a prospective cohort study, which will recruit 300 transgender women of colour (150 Black/African American, 100 Latina and 50 Asian/Pacific Islander participants) in New York City. There will be three waves of data collection separated by 6 months. At each wave, participants will provide information on their relationships, social and sexual networks, and neighbourhoods. Global position system technology will be used to generate individual daily path areas in order to estimate neighbourhood-level exposures. Multivariate analyses will be conducted to assess cross-sectional and longitudinal, independent and synergistic associations of personal relationships (notably individual social capital), social and sexual networks, and neighbourhood factors (notably neighbourhood-level social cohesion) with PrEP uptake and discontinuation. ETHICS AND DISSEMINATION: The TURNNT protocol was approved by the Columbia University Institutional Review Board (reference no. AAAS8164). This study will provide novel insights into the relationship, network and neighbourhood factors that influence HIV prevention behaviours among transgender women of colour and facilitate exploration of this population's health and well-being more broadly. Through community-based dissemination events and consultation with policy makers, this foundational work will be used to guide the development and implementation of future interventions with and for transgender women of colour.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".