Promoting Diversity in the Recruitment of a Youth Advisory Council in the Mental Health and Addictions System
Bibliographic record
Abstract
INTRODUCTION: There is increasing interest in youth advisory councils (YACs) within the mental health and addictions (MHA) system; however, little is known about methods to ensure diversity of perspectives on these councils. Diverse perspectives are critical for ensuring that youth MHA organizations are equipped to support youth needs. METHODS: A youth-led YAC recruitment initiative was developed and implemented at a GTA-based youth MHA navigation service: the Family Navigation Project. RESULTS: Specifically, this paper highlights the efforts of three youth staff and volunteers who championed a YAC recruitment plan focused on promoting equity, diversity, inclusion (EDI) and accessibility, youth engagement (YE), and lived experience/expertise (LE). DISCUSSION: This paper is written from the perspective of the youth recruitment team and provides important insights for MHA services interested in ensuring EDI, accessibility, YE and LE on a new or existing YAC, and researchers studying YE in MHA care.
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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 | 0.068 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".