A global research synthesis of HIV and STI biobehavioural risks in female-to-male transgender adults
Bibliographic record
Abstract
There is a growing interest in HIV infection and sexually transmitted infection (STI) disease burden and risk among transgender people globally; however, the majority of work has been conducted with male-to-female transgender populations. This research synthesis comprehensively reviews HIV and STI research in female-to-male (FTM) transgender adults. A paucity of research exists about HIV and STIs in FTMs. Only 25 peer-reviewed papers (18 quantitative, 7 qualitative) and 11 'grey literature' reports were identified, most in the US or Canada, that include data identifying HIV and STI risks in FTMs (five with fully laboratory-confirmed HIV and/or STIs, and five with partial laboratory confirmation). Little is known about the sexual and drug use risk behaviours contributing to HIV and STIs in FTMs. Future directions are suggested, including the need for routine surveillance and monitoring of HIV and STIs globally by transgender identity, more standardised sexual risk assessment measures, targeted data collection in lower- and middle-income countries, and explicit consideration of the rationale for inclusion/exclusion of FTMs in category-based prevention approaches with MSM and transgender people. Implications for research, policy, programming, and interventions are discussed, including the need to address diverse sexual identities, attractions, and behaviours and engage local FTM communities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".