Development of a family-level intervention for families with transgender and/or nonbinary youth: Lessons and recommendations.
Bibliographic record
Abstract
Family support plays an important role in promoting resilience and health among transgender and/or nonbinary youth (TNBY), but family members often experience barriers to supporting their TNBY, including minority-adjacent stress stemming from exposure to structural stigma and antitransgender legislation. TNBY and their families need effective family-level interventions developed using community-based participatory research (CBPR), which integrates community members (e.g., TNBY, family members, service providers for families with TNBY) into the intervention development process to ensure the resulting intervention is relevant and useful. Informed by findings from the Trans Teen and Family Narratives Project, we used CBPR to develop the Trans Teen and Family Narratives Conversation Toolkit, a family-level intervention designed to educate families about TNBY and facilitate conversations about gender. The toolkit was developed across 1.5 years (June 2019 to January 2021) using four integrated phases: (1) content development: digital storytelling workshop with TNBY; (2) content review: digital storyteller interviews and user focus groups; (3) content development: study team content synthesis and website development; and (4) content review: website review by TNBY, family members, and mental health providers, and intervention refinement. This article outlines the intervention development process, describes strategies employed to navigate challenges encountered along the way, and shares key learnings to inform future CBPR intervention development efforts. (PsycInfo Database Record (c) 2024 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".