Narratives as mirrors and doors: A multiple-method pilot evaluation of an online, family-level intervention for families with transgender and/or nonbinary youth.
Bibliographic record
Abstract
Transgender and/or nonbinary (TNB) youth experience substantial stigma and discrimination in comparison to cisgender peers. While family support can buffer negative psychological effects of stigma and discrimination, TNB youth experience lower levels of family support. Family-level and narrative-based interventions are understudied strategies for addressing this challenge. We conducted a pilot test of the feasibility and acceptability of the Trans Teen and Family Narratives (TTFN) Conversation Toolkit, an online, narrative-based intervention for families of TNB youth. Participants included seven mental health providers (MHPs), nine TNB youth ages 13-21 years, and 21 parents/caregivers and siblings in the United States. TNB youth participants included two girls, three boys, and four nonbinary youth. Of 28 Caregivers, siblings, and MHPs, 16 were cisgender women. Most participants were White. We conducted surveys from 2021-2022 at multiple timepoints (baseline, end-of-testing, and 3- and 6-month follow-ups) and conducted interviews at end-of-testing. We analyzed interviews using immersion/crystallization and thematic analysis. Major themes included: 1) external and intrapersonal factors affecting toolkit usage, 2) participant experiences of character identification within toolkit content, and 3) participant recommendations. At end-of-testing, 24 of 34 retained participants watched six or more of the toolkit's eight digital stories and 28 used the toolkit for the expected amount of time. The TTFN Conversation Toolkit is a feasible and acceptable tool for TNB youth and families. The toolkit may serve as a resource to enhance clinical practice and mitigate the impacts of a harmful sociopolitical climate on TNB youth mental health.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".