“It’s a Big Ordeal”: A Mixed Methods Study of the Experiences of non-HIV STI Testing Among Trans and Gender Diverse People
Bibliographic record
Abstract
Objectives: Transgender and gender diverse (TGD) persons are disproportionately affected by sexually transmitted infection (STI) inequities. Research predominantly focuses on HIV disparities among transfeminine persons, whereas non-HIV STIs (e.g., chlamydia) and transmasculine and nonbinary persons are overlooked. Thus, we examined barriers and facilitators to uptake of non-HIV STI testing among TGD persons, inclusive of transmasculine, transfeminine, and nonbinary persons. Methods: This community-based explanatory sequential mixed-methods study utilized secondary quantitative data collected 2018-2019 from the Michigan Trans Health Survey (n = 528) analyzed utilizing logistic regression to test associations between social ecological hypothesized factors and non-HIV STI testing. Primary qualitative focus group data collected 2022 (n = 36 TGD participants) were analyzed using a reflexive thematic approach. Results: In multivariable analyses adjusting for age and race, reporting a very/somewhat inclusive primary care provider and ever experiencing sexual violence were statistically significantly positively associated with testing. Five themes were identified that illustrated the complexity of the testing process: 1) The "why" motivating testing; 2) "I've been vocal": The impact of individual agency on TGD peoples' testing practices; 3) "It's a big ordeal": Running the gauntlet of testing; 4) "Doesn't give me a hassle": Gratitude for bare minimum dignity when accessing care; and, 5) "Open, honest, and transparent": Increased testing access due to collaborative, judgment-free, and trustworthy patient-provider relationships. Conclusions: Findings inform future interventions to increase STI testing among TGD populations, such as enhanced trauma-informed, intersectional, and gender-affirming STI testing, across urgent care, sexual and reproductive healthcare, and primary care. Findings call for systems-level change to promote such care to increase STI testing and advance health equity among TGD populations.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".