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Record W4407243749 · doi:10.1186/s12913-024-12101-w

Using a co-design approach to develop a Preventative Online Mental Health Program for Youth (POMHPY): a quality improvement project

2025· article· en· W4407243749 on OpenAlexaffabout
Elnaz Moghimi, Kimberly D. Belfry, Sarah Farr, Shavon Stafford, Arina Bogdan, Megan Brush, Christopher Canning, Soyeon Kim

Bibliographic record

VenueBMC Health Services Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWaypoint Centre for Mental Health CareQueen's UniversityMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsHealth informaticsNursing researchHealth administrationMental healthPublic healthMedicineQuality (philosophy)Quality managementNursingPsychiatryOperations managementEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0040.002
Open science0.0030.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.859
GPT teacher head0.785
Teacher spread0.074 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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