A multisector study assessing readiness for providing gender-affirming services to transgender women.
Bibliographic record
Abstract
OBJECTIVE: Transgender women experience significant health disparities and are at disproportionately high risk of violence, stigmatization, and discrimination. Stigma and discrimination are enacted by staff and leaders in multiple public and service sectors including housing, physical and behavioral health, criminal justice and law enforcement, workforce development, and education. Multisectoral approaches recognize that health involves coordination and cooperation across various sectors, government departments, and stakeholders. The purpose of this study was to assess readiness and capacity to provide gender-affirming services. METHOD: The Division Director of Access to Prevention, Advocacy, Intervention and Treatment/Special Services for Groups a nonprofit organization recruited participants via email from the following 10 sectors: behavioral health, medical, disability, law enforcement, criminal justice, housing, workforce development, faith-based, legal aid, and education. In-depth interviews were conducted with 44 participants. Interviews were transcribed and analyzed using a framework approach, a method of deductive analysis. Participants (n = 27) attended a follow-up meeting that included a presentation of the findings from the interview analysis and held small group discussions about sector capacity for gender-affirming services. RESULTS: Findings indicated that the capacity of providing training, leadership support, and policies varied. Gaps included a lack of funding for trainings and programs, the need for a more exclusive focus on transgender issues, and inclusion of transgender women participation to ensure the inclusion of their lived experiences. CONCLUSION: Results suggest that a multisectoral approach is feasible. Across participants, best practices included clear and explicit trans-affirming policies, establishment of safe spaces/physical indicators of allyship, and representation within organizations of members of the lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexual identities community (including employment and visible supportive images). (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".