Assessing the impacts and outcomes of youth driven mental health promotion: A mixed‐methods assessment of the Social Networking Action for Resilience study
Bibliographic record
Abstract
Mental health challenges are the leading health issue facing youth globally. To better respond to this health challenge, experts advocate for a population health approach inclusive of mental health promotion; yet this area remains underdeveloped. Further, while there is growing emphasis on youth-engaged research and intervention design, evidence of the outcomes and impacts are lacking. The purpose of this paper is to contribute to addressing these gaps, presenting findings from the Social Networking Action for Resilience (SONAR) study, an exploration of youth-driven mental health promotion in a rural community in British Columbia, Canada. Mixed methods including pre- and post-intervention surveys (n = 175) and qualitative interviews (n = 10) captured the outcomes and impacts of the intervention on indicators of mental health, the relationship between level of engagement and benefit, and community perceptions of impact. Findings demonstrate the feasibility and benefits of youth engaged research and intervention at an individual and community-level.
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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 | 0.031 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".