Knowledge Mobilization and Mental Health Policy: Lessons from the Canadian Consensus Conference on the Mental Health of Emerging Adults
Bibliographic record
Abstract
This case study explores how the Consensus Conference on the Mental Health of Emerging Adults, hosted by the Mental Health Commission of Canada in 2015, can impact mental health policy and practice in Canada. The study draws on interviews from 14 delegates who attended the conference. Participants praised innovations such as the inclusion of an emerging adult panel that provided feedback on all discussions, and efforts to make the consensus-building process meaningful in the Canadian policy context. Findings suggest that consensus conferences are a promising way to operationalize the Mental Health Strategy for Canada through policy and practice changes.
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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 | 0.063 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.067 | 0.034 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".