Using a co-design approach to develop a Preventative Online Mental Health Program for Youth (POMHPY): a quality improvement project
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, youth in Ontario, Canada experienced a steep rise in mental health concerns. Preventative intervention programs can address the psychological impact of the pandemic on youth and build resiliency. Co-design approaches to developing such programs actively involve young people, resulting in solutions tailored to their unique needs. The current paper details the co-design approach to creating a Preventative Online Mental Health Program for Youth (POMHPY)-a virtually delivered program designed for Ontario youth ages 12 to 25 that promotes mental, physical, and social wellbeing. METHODS: The Participatory Action Research (PAR) framework guided the development of the initiative. Literature reviews were conducted to identify existing evidence-based programs targeting youth. Youth perspectives were primarily gathered via the Youth Advisory Group, comprising a Youth Resilience Coordinator and a Youth Engagement Lead, who contributed to a literature review, surveys, focus groups, and program assets. Community insights were gathered through Community Reference Group (CRG) meetings, which engaged participants from local and provincial organizations, as well as individuals either directly representing or affiliated at arm's length with youth. RESULTS: A review of the current literature highlighted the importance of regular physical activity, social connectedness, good sleep hygiene, and healthy family relationships to emotional wellbeing. Survey findings informed program session length, duration, delivery, and activities. Focus groups expanded on the survey findings and provided an in-depth understanding of youth preferences for program delivery. CRG meetings captured community insights on program refinements to better meet the needs of youth. As such, the development of POMHPY was a collaborative effort among researchers, youth, and community partners. CONCLUSIONS: The findings highlight the value of co-design and PAR-informed approaches in developing youth-targeted online wellbeing programs, providing actionable insights for iterative improvements and future pilot testing. The resulting 6-week program, led by youth facilitators, will focus on teaching mental, social, and physical wellness strategies and skills through various evidence-based, interactive activities.
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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 | 0.048 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".