Improving Mental Health Among Transgender Adolescents: Implementing Mindful Self-Compassion for Teens
Bibliographic record
Abstract
The purpose of this study was to investigate the feasibility, acceptability, and preliminary outcomes of an online self-compassion intervention for transgender adolescents, with the aim of improving mental health. Participants identified as transgender or gender expansive, were between the ages of 13 and 17, and lived in the U.S. or Canada. The empirically-based self-compassion program, Mindful Self-Compassion for Teens (formerly Making Friends with Yourself) was implemented in eight 1.5 hour sessions on the Zoom platform by two trained instructors. Surveys were administered pre-, post-intervention, and at 3 months follow-up, and qualitative data were collected through end-of-program interviews and open-ended questions on the post-survey. All protocols were approved by the university IRB. Quantitative data analysis included repeated measures ANOVAs, and qualitative data were analyzed via both inductive and deductive methods. Results indicated that all but one psychosocial measure significantly improved from pre- to post-intervention, which then significantly improved at 3-month follow-up; most other improvements were maintained at follow-up. Four themes emerged from the qualitative data: virtual safe space; connection to body; personal growth; and recommended course changes and are discussed. Results suggest that self-compassion interventions can be incorporated into therapy programs to support and improve mental health for transgender adolescents.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".