Transforming Mental Health for Transgender and Gender-Diverse Young Adults Using Interventions to Drive Equity (TransHealthGUIDE): Protocol for a Digital Randomized Controlled Trial
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,004 |
| Méta-épidémiologie (sens large) | 0,011 | 0,006 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,134 | 0,019 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».