Implementation of a youth‐adult partnership model in youth mental health systems research: Challenges and successes
Bibliographic record
Abstract
BACKGROUND: By integrating Youth-Adult Partnerships (Y-APs) in organizational decision making and programming in health-care settings, youth can be engaged in decisions that affect them in a way that draws on their unique skills and expertise. Despite challenges, Y-APs can have many benefits for youth and adults alike, as well as for the programmes and initiatives that they undertake together. OBJECTIVE: This article describes the development, implementation and success of a Y-AP initiative at the McCain Centre at the Centre for Addiction and Mental Health, a large urban hospital. METHOD: The McCain Y-AP implementation model was developed based on the existing literature, guided by the team's progressive experience. The development and implementation procedure is described, with indicators of the model's success and recommendations for organizations interested integrating youth engagement. RESULTS: The McCain Y-AP has integrated youth into a wide range of mental health and substance use-related initiatives, including research projects, conferences and educational presentations. The model of youth engagement is flexible to include varying degrees of involvement, allowing youth to contribute in ways that fit their availability, interest and skills. Youth satisfaction has been strong and both the youth and adult partners have learned from the experience. DISCUSSION: Through the McCain Y-AP initiative, youth engagement has helped advance numerous initiatives in a variety of ways. Flexible engagement, multifaceted mentorship, reciprocal learning and authentic decision making have led to a successful partnership that has provided opportunities for growth for all those involved. Health-care organizations interested in engaging youth can learn from the McCain Y-AP experience to guide their engagement initiatives and maximize success.
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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 | 0.185 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".