Digital-Assisted Self-interview of HIV or Sexually Transmitted Infection Risk Behaviors in Transmasculine Adults: Development and Field Testing of the Transmasculine Sexual Health Assessment
Bibliographic record
Abstract
BACKGROUND: The sexual health of transmasculine (TM) people-those who identify as male, men, or nonbinary and were assigned a female sex at birth-is understudied. One barrier to conducting HIV- and sexually transmitted infection (STI)-related research with this population is how to best capture sexual risk data in an acceptable, gender-affirming, and accurate manner. OBJECTIVE: This study aimed to report on the community-based process of developing, piloting, and refining a digitally deployed measure to assess self-reported sexual behaviors associated with HIV and STI transmission for research with TM adults. METHODS: A multicomponent process was used to develop a digital-assisted self-interview to assess HIV and STI risk in TM people: gathering input from a Community Task Force; working with an interdisciplinary team of content experts in transgender medicine, epidemiology, and infectious diseases; conducting web-based focus groups; and iteratively refining the measure. We field-tested the measure with 141 TM people in the greater Boston, Massachusetts area to assess HIV and STI risk. Descriptive statistics characterized the distribution of sexual behaviors and HIV and STI transmission risk by the gender identity of sexual partners. RESULTS: The Transmasculine Sexual Health Assessment (TM-SHA) measures the broad range of potential sexual behaviors TM people may engage in, including those which may confer risk for STIs and not just for HIV infection (ie, oral-genital contact); incorporates gender-affirming language (ie, genital or frontal vs vaginal); and asks sexual partnership characteristics (ie, partner gender). Among 141 individual participants (mean age 27, SD 5 years; range 21-29 years; n=21, 14.9% multiracial), 259 sexual partnerships and 15 sexual risk behaviors were reported. Participants engaged in a wide range of sexual behaviors, including fingering or fisting (receiving: n=170, 65.6%; performing: n=173, 66.8%), oral-genital sex (receiving: n=182, 70.3%; performing: n=216, 83.4%), anal-genital sex (receptive: n=31, 11.9%; insertive: n=9, 3.5%), frontal-genital sex (receptive: n=105, 40.5%; insertive: n=46, 17.8%), and sharing toys or prosthetics during insertive sex (n=62, 23.9%). Overall barrier use for each sexual behavior ranged from 10.9% (20/182) to 81% (25/31). Frontal receptive sex with genitals and no protective barrier was the highest (21/42, 50%) with cisgender male partners. In total, 14.9% (21/141) of participants reported a lifetime diagnosis of STI. The sexual history tool was highly acceptable to TM participants. CONCLUSIONS: The TM-SHA is one of the first digital sexual health risk measures developed specifically with and exclusively for TM people. TM-SHA successfully integrates gender-affirming language and branching logic to capture a wide array of sexual behaviors. The measure elicits sexual behavior information needed to assess HIV and STI transmission risk behaviors. A strength of the tool is that detailed partner-by-partner data can be used to model partnership-level characteristics, not just individual-level participant data, to inform HIV and STI interventions.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".