Mapping Community-Engaged Implementation Strategies with Transgender Scientists, Stakeholders, and Trans-Led Community Organizations
Bibliographic record
Abstract
Abstract Purpose of Review Pre-exposure prophylaxis (PrEP) represents one of the most effective methods of prevention for HIV, but remains inequitable, leaving many transgender and nonbinary (trans) individuals unable to benefit from this resource. Deploying community-engaged PrEP implementation strategies for trans populations will be crucial for ending the HIV epidemic. Recent Findings While most PrEP studies have progressed in addressing pertinent research questions about gender-affirming care and PrEP at the biomedical and clinical levels, research on how to best implement gender-affirming PrEP systems at the social, community, and structural levels remains outstanding. Summary The science of community-engaged implementation to build gender-affirming PrEP systems must be more fully developed. Most published PrEP studies with trans people report on outcomes rather than processes, leaving out important lessons learned about how to design, integrate, and implement PrEP in tandem with gender-affirming care. The expertise of trans scientists, stakeholders, and trans-led community organizations is essential to building gender-affirming PrEP systems.
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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 | 0.053 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".