Transforming Mental Health for Transgender and Gender-Diverse Young Adults Using Interventions to Drive Equity (TransHealthGUIDE): Protocol for a Digital Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Transgender, nonbinary, and gender diverse (TGD) young adults face a disproportionately high mental health burden, including 2- to 4-fold increased risk of suicidality and depressive distress compared to their cisgender peers. Family and community support are protective factors that may mitigate these adverse outcomes, representing key targets for mental health interventions. OBJECTIVE: This randomized controlled trial (RCT) evaluates the effectiveness of TransHealthGUIDE, a novel digital, app-based education and support intervention aimed at reducing past 3-month suicidal ideation and past 2-week depressive distress among TGD young adults. Participants are randomized 1:1 to an immediate intervention arm or a 6-month deferred intervention arm (waitlist control). METHODS: From 2021 to 2024, a multidisciplinary team of academic and community partners conducted formative research and codeveloped a digital app containing 33 self-paced modules, communication, and interactive resources. The content is grounded in the Social Ecological Suicide Prevention Model and the Gender Minority Stress and Resilience Framework, designed to enhance family support and community connection for TGD young adults and their families. TGD young adults (ages 18-24 years) and their caregivers are prospectively enrolled and randomized in 10 US states (California, Connecticut, Illinois, Maryland, Massachusetts, New Jersey, New York, Oregon, Vermont, and Washington, DC). Participants complete self-reported surveys at baseline, 6 months, and 12 months, assessing sociodemographics, family support, community connection, suicidal ideation, depressive distress, and intervention acceptability. App usage and engagement are tracked via metadata. The target sample size is 500 TGD young adults, with 50% (n=250) identifying as people of color and 125 TGD young adult-caregiver dyads. RESULTS: TGD young adult and caregiver communities, including our community advisory board (CAB), have guided all aspects of the project: study design (eg, waitlist control), survey development (eg, self-reported rather than clinician-administered suicidality measures and inclusion of social justice activism as a form of support), intervention content (eg, text, infographics, interviews with TGD people and caregivers, and simulations), and digital app safety features (eg, a quick-exit button for privacy). Recruitment and study screening began on January 21, 2025. As of May 2, 2025, a total of 149 individuals completed a study screener, with 132 (88.6%) of 149 found eligible. Enrollment visits began on April 23, 2025. As of May 2, 2025, five TGD young adults have been enrolled. Ongoing engagement with the CAB will inform iterative improvements throughout the trial. CONCLUSIONS: Scalable, app-based interventions tailored to meet the needs of TGD young people hold promise for population-level mental health impact. This RCT-targeting suicidality and depressive distress via education and support for both TGD young adults and their caregivers-addresses a critical gap in mental health interventions for this underserved population. TRIAL REGISTRATION: ClinicalTrials.gov NCT06177600; https://clinicaltrials.gov/study/NCT06177600. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78619.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.134 | 0.019 |
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".