Gender Affirming Psychotherapy (GAP): Core principles and skills to reduce the mental healthcare "gap" for transgender youth
Bibliographic record
Abstract
Objectives: Transgender youth are significantly more likely to need mental healthcare than cisgender youth due to their high exposure to discrimination and victimization – including within mental healthcare. Accordingly, transgender youth have relatively low care satisfaction and high treatment drop-out, further exacerbating extant mental health inequities. To reduce these inequities, mental health providers need knowledge and skills to enhance transgender youth’s treatment engagement and benefits. However, a comprehensive set of practices addressing the needs of transgender youth patients and their providers does not exist. Thus, the current study developed the Gender Affirming Psychotherapy (GAP) intervention.Methods: GAP was developed using human-centered design, a methodological approach for creating interventions that prioritizes the needs of key stakeholders, which in this study included mental health providers, transgender youth, and their parents. A scoping review of the literature and stakeholder focus groups were conducted to identify GAP, which encompasses core principles and skills to enhance mental health services for transgender youth.Results: GAP encompasses 26 principles and 39 skills, organized within 10 domains. All principles and skills were designed to be relevant for various provider types (e.g., psychiatrists, social workers) and to be flexibly adapted to meet diverse patient needs.Conclusions: GAP offers a scalable and flexible approach to addressing the growing mental healthcare needs of transgender youth. This study also suggests that human-centered design is a feasible and efficient method for developing interventions to address health inequities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".