Equipping youth for meaningful policy engagement: an environmental scan
Bibliographic record
Abstract
To better address the mental health and substance use crises facing youth globally, a comprehensive approach, inclusive of mental health promotion is needed. A key component of mental health promotion is policy intervention to address the social and structural determinants of health. Importantly, youth should be engaged in these efforts to maximize relevancy and impact. Yet, while there is growing interest in the inclusion of youth in the policymaking process, there is a paucity of guidance on how to do this well. This environmental scan reports findings from a comprehensive search of academic and grey literature that was conducted using the electronic databases: CINAHL, ERIC, MEDLINE, PsycINFO, Google Scholar, and Google. Search terms included variations of 'youth*', 'educat*', 'engage*', 'policy' and 'policy training'. Thirteen English language training programmes met inclusion criteria. Analysis identified marked differences in programme philosophy and focus by geographic region and highlights the need for enhanced evaluation and impact measurement moving forward. This paper makes a needed contribution to the evidence-base guiding this key mental health promotion strategy, which holds the potential to address critical gaps in approaches to youth mental health and substance use.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".