Refining the Universal, School-Based OurFutures Mental Health Program to Be Trauma Informed, Gender and Sexuality Diversity Affirmative, and Adherent to Proportionate Universalism: Mixed Methods Participatory Design Process
Bibliographic record
Abstract
BACKGROUND: Mental disorders are the leading cause of disease burden among youth. Effective prevention of mental disorders during adolescence is a critical public health strategy to reduce both individual and societal harms. Schools are an important setting for prevention; however, existing universal school-based mental health interventions have shown null, and occasionally iatrogenic, effects in preventing symptoms of common disorders, such as depression and anxiety. OBJECTIVE: This study aims to report the adaptation process of an established, universal, school-based prevention program for depression and anxiety, OurFutures Mental Health. Using a 4-stage process; triangulating quantitative, qualitative, and evidence syntheses; and centering the voices of young people, the revised program is trauma-informed; lesbian, gay, bisexual, transgender, nonbinary, queer, questioning, and otherwise gender and sexuality diverse (LGBTQA+) affirmative; relevant to contemporary youth; and designed to tailor intervention dosage to those who need it most (proportionate universalism). METHODS: Program adaptation occurred from April 2022 to July 2023 and involved 4 stages. Stage 1 comprised mixed methods analysis of student evaluation data (n=762; mean age 13.5, SD 0.62 y), collected immediately after delivering the OurFutures Mental Health program in a previous trial. Stage 2 consisted of 3 focus groups with high school students (n=39); regular meetings with a purpose-built, 8-member LGBTQA+ youth advisory committee; and 2 individual semistructured, in-depth interviews with LGBTQA+ young people via Zoom (Zoom Video Communications) or WhatsApp (Meta) text message. Stage 3 involved a clinical psychologist providing an in-depth review of all program materials with the view of enhancing readability, improving utility, and normalizing emotions while retaining key cognitive behavioral therapy elements. Finally, stage 4 involved fortnightly consultations among researchers and clinicians on the intervention adaptation, drawing on the latest evidence from existing literature in school-based prevention interventions, trauma-informed practice, and adolescent mental health. RESULTS: Drawing on feedback from youth, clinical psychologists, and expert youth mental health researchers, sourced from stages 1 to 4, a series of adaptations were made to the storylines, characters, and delivery of therapeutic content contained in the weekly manualized program content, classroom activities, and weekly student and teacher lesson summaries. CONCLUSIONS: The updated OurFutures Mental Health program is a trauma-informed, LBGTQA+ affirmative program aligned with the principles of proportionate universalism. The program adaptation responds to recent mixed findings on universal school-based mental health prevention programs, which include null, small beneficial, and small iatrogenic effects. The efficacy of the refined OurFutures Mental Health program is currently being tested through a cluster randomized controlled trial with up to 1400 students in 14 schools across Australia. It is hoped that the refined program will advance the current stalemate in universal school-based prevention of common mental disorders and ultimately improve the mental health and well-being of young people in schools.
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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 | 0.091 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".