Engaging a community for youth mental health and wellness: Reflections and lessons learned
Bibliographic record
Abstract
As clinicians at a university-affiliated health centre faced with youth mental health and substance use concerns, we reached out to the local community for guidance. We partnered with community leaders to explore how to best understand the issues and engage with the community. Using a community-engaged research (CEnR) approach, we conducted a needs assessment to explore the issues and inform change. We formalised a partnership with the local school and community board, which led to the creation of a Community Alliance. Our engagement efforts allowed us to understand the community more deeply and establish more effective change. Our most successful outcome was the development of a youth mental health and wellness Action Plan which helped direct our strategies moving forward. This article highlights our community engagement activities, processes and lessons learned, which may be of benefit to other academic researchers and clinicians who are interested in CEnR.
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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 | 0.050 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.012 | 0.022 |
| 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".