HIV epidemics among transgender populations: the importance of a trans-inclusive response
Bibliographic record
Abstract
Transgender people are severely underserved in the global response to HIV.Less than 40% of countries report that their national AIDS strategies address transgender people [1], despite a growing body of evidence that transgender women, in particular, face a disproportionate and heavy burden of HIV.An estimated 19% of transgender women worldwide are living with HIV, and they have almost 50 times the odds of living with HIV compared to other reproductive age adults [2].The impact among transgender sex workers is even more profound.Transgender women sex workers have a prevalence of HIV that is nine times that of non-transgender female sex workers and three times that of male sex workers [3].Data on HIV among transgender men are extremely limited.However, emerging studies among transgender men who have sex with men (MSM) suggest heightened HIV vulnerability among this group.While specific data on transgender men are lacking, in settings with high HIV prevalence and epidemics of gender-based violence, sexual assault on gender variant persons places transgender men and women at substantial risk for HIV as well as other negative sequelae of sexual violence.The stigma, violence and human rights abuses transgender people suffer drive much of their risk for HIV and hinder their access to care.While the world's response to HIV has largely overlooked transgender people and the myriad factors that increase their risk, the tide is slowly turning.In 2014, the World Health Organization provided guidance on the essential elements of HIV programming among key populations, including the first specific recommendations for transgender people [4].More recently, leaders in transgender health have spearheaded the development and launch of the first practical guide for implementing HIV and STI programmes with transgender people [5].These documents represent important steps forward in the global HIV response.However, in order to provide the most effective interventions to the populations with the greatest need, we need research that is specific to the unique concerns of transgender communities.This calls for research to be accessible to all who seek to implement transgender-competent, evidence-based programmes.This special issue of the Journal of the International AIDS Society is dedicated to that goal.We issued a global call for abstracts addressing topics relevant to HIV in transgender populations, and submissions
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".