A Trans Youth of Color Study to Measure Health and Wellness: Protocol for a Longitudinal Observation Study
Bibliographic record
Abstract
BACKGROUND: Growing research on transgender youth is accounting for the variety of ways in which young people define their genders and sexualities. Because of this growing representation, more research is needed to understand how intersectional identities and stigma affect risk for HIV acquisition along the HIV care continuum and engagement in mental and physical health care. Little is known about accessibility to HIV-related prevention services of nonbinary and transmasculine youth, and further understanding of the impacts on transfeminine people-those who have historically faced the highest prevalence of HIV positivity-is crucial. OBJECTIVE: The overarching aims of the Trans Youth of Color Study are to conduct longitudinal research with a cohort of transgender minority youth (TGMY), explore factors that aid in the prevention of new HIV infection and transmission, and reduce HIV- and AIDS-related disparities by focusing on successful engagement in care. Findings from this research will be used to inform the development of new interventions designed to engage TGMY in the HIV prevention and care continua. METHODS: Longitudinal research (baseline and follow-up assessments every 6 months for 3 waves of data collection) followed a cohort (N=108) of transgender youth of color recruited in Los Angeles, California, United States. Participants were recruited using multiple community-informed strategies, such as from local venues, social media, and participant referral. In addition to self-report surveys, urine was collected to assess recent use of illicit drugs, and blood, rectal, and throat swabs were collected to test for current sexually transmitted infection and HIV infection. Additional blood and plasma samples (10 mL for 4 aliquots and 1 pellet) were collected and stored for future research. RESULTS: Participants in the Trans Youth of Color Study were recruited between May 25, 2018, and December 7, 2018. Baseline and longitudinal data are being analyzed as of August 2022. CONCLUSIONS: The findings from this research will inform adaptations to existing evidence-based HIV prevention interventions and help to guide new interventions designed to engage TGMY, especially those who are Black, Indigenous, or people of color, in the HIV prevention and care continua. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39207.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".