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Record W4414208580 · doi:10.1192/j.eurpsy.2025.697

Using a co-design approach to develop, implement, and evaluate a Preventative Online Mental Health Program for Youth (POMHPY)

2025· article· en· W4414208580 on OpenAlexaffabout
Sun-Mi Kim, Elnaz Moghimi

Bibliographic record

VenueEuropean Psychiatry · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsMental healthFocus groupProgram evaluationDescriptive statisticsQuality (philosophy)Mental illness

Abstract

fetched live from OpenAlex

Introduction From March 2020 to 2021, the risk of youth developing a mental health issue increased by 50% in Canada. To address the detrimental effects of the COVID-19 pandemic, this project collaborated with youth and community partners in Ontario, Canada, to co-design a Preventative Online Mental Health Program for Youth (POMHPY) focused on improving mental, physical, and social well-being. Objectives (1) To co-design a preventative online mental health program tailored to the needs of Ontario youth. (2) To evaluate the program’s efficacy in improving mental well-being and health-related quality of life. (3) To engage youth in the development and continuous improvement of the program. Methods Initially, literature reviews were conducted to identify evidence-based programs that could be integrated into POMHPY. Surveys and focus groups were used to capture youths’ mental health concerns and program needs. The findings were presented to community partners for additional feedback and refinement of the program. A second survey and focus group explored the likelihood of program use and piloted the first session. Subsequently, 53 youths (mean age=19.15) participated in the POMHPY program during the summer of 2023. Pre-, post-, and follow-up surveys measuring mental well-being were administered. Preliminary descriptive statistics and t-test analysis were conducted to measure the program’s efficacy. A subset of participants (n = 21) attended 90-minute focus groups to discuss program perceptions, perceived benefits, impact on personal life, and areas of improvement. Results Youths’ mental well-being, measured by the Warwick-Edinburgh Mental Well-being Scale, significantly improved after the completion of the program [t (24) =-2.91, p=.008]. Health-related quality of life, measured by the AqoL-6D, also significantly improved [t (6) =-3.34, p=.016]. These improvements were maintained one month after completing the program. Participants viewed the skills and strategies learned in POMHPY as beneficial in improving their stress and well-being. Peer facilitators in the same age range as participants contributed to meaningful discussions and interactive activities that contrasted with a lecture-style learning environment. Suggestions for improvement included flexible scheduling, increasing reminders, and enhancing understanding of program components. Conclusions Preliminary analysis supports the program’s efficacy in improving mental well-being and health-related quality of life. Participants also reported a positive experience with the program and suggested improvements for integration. The program will be scaled nationally in the next phase, ensuring broader access to preventative mental health care for youth across Canada. Disclosure of Interest None Declared

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)0.046
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.190
GPT teacher head0.514
Teacher spread0.324 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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