Gender-affirming healthcare for incarcerated transgender and gender diverse people: An international scoping review
Bibliographic record
Abstract
Background: Transgender and gender diverse (TGD) people face unique challenges and have distinct needs while incarcerated. Gender-affirming healthcare improves mental health outcomes and supports gender transition. While in recent years, correctional institutions have begun to recognize and address the gender-affirming healthcare needs of TGD people, there is a lack of understanding and awareness of TGD people's experiences when accessing gender-affirming healthcare while incarcerated. Aims/Method: We conducted a scoping review on international empirical research published from 2018 to 2024 on TGD people's experiences with gender-affirming healthcare while incarcerated using the Joanna Briggs Institute methodology. A search of the databases Web of Science, PsycInfo, and PubMed was completed on May 6, 2024. The data was analyzed using thematic analysis with an abolition feminist framework and transformative approach. Results: Our search yielded 15 studies published between 2018 and 2024, across eight countries. The studies included qualitative and quantitative method designs. The main outcomes of interest were hormone replacement therapy (HRT), staff competency and training, institutional policies, gender-affirming surgeries, and mental health. Conclusions: Findings from this review highlight the need for consistent, adequate, trans-informed gender-affirming healthcare for all TGD people experiencing incarceration. TGD people frequently face institutional and interpersonal barriers when trying to access gender-affirming healthcare. There is a need for culturally-informed training for correctional staff and clear policies in correctional settings to ensure the delivery of adequate and gender-affirming healthcare.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".