Peer-based interventions to support transgender and gender diverse people’s health and healthcare access: A scoping review
Bibliographic record
Abstract
Background: Pervasive health and healthcare disparities experienced by transgender (trans) and gender diverse (TGD) people require innovative solutions. Peer-based interventions may address disparities, and are an approach endorsed by TGD communities. However, the scope of the literature examining peer-based interventions to address health and healthcare access inclusive of TGD people is uncharted.Aim: This scoping review aimed to understand the extent of the literature about peer-based interventions conducted with and/or inclusive of TGD populations; specifically, study participants (e.g. sociodemographics), study designs/outcomes, intervention components (e.g. facilitator characteristics), and intervention effectiveness.Methods: Underpinned by Arksey and O’Malley’s framework: (1) identifying the research question; (2) identifying studies; (3) study selection; (4) charting data; and (5) collating, summarizing, and reporting results, eligible studies were identified, charted, and thematically analyzed. Databases (e.g. ProQuest) and snowball searching were utilized to identify peer-reviewed literature published within 15 years of February 2023. Extracted data included overarching study characteristics (e.g. author[s]), methodological characteristics (e.g. type of research), intervention characteristics (e.g. delivery modality), and study findings.Results: Thirty-six eligible studies documented in 38 peer-reviewed articles detailing 40 unique peer-based interventions were identified. Forty-four percent (n = 16/36) of studies took place in United States (U.S.) urban centers. Over half (n = 23/40, 58%) focused exclusively on TGD people, nearly three-quarters of which (n = 17/23, 74%) focused exclusively on trans women/transfeminine people. Ninety-two percent (n = 33/36) included quantitative methods, of which 30% (n = 10/33) were randomized controlled trials. HIV was a primary focus (n = 30/36, 83.3%). Few interventions discussed promotion of gender affirmation for TGD participants. Most studies showed positive impacts of peer-based intervention.Discussion: Although promising in their effectiveness, limited peer-based interventions have been developed and/or evaluated that are inclusive of gender-diverse TGD people (e.g. trans men and nonbinary people). Studies are urgently need that expand this literature beyond HIV to address holistic needs and healthcare barriers among TGD communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".