Policy options for a pan-Canadian mental health and substance use health workforce strategy
Bibliographic record
Abstract
Canada needs a systematically developed, fit-for-purpose Mental Health and Substance Use Health (MHSUH) workforce strategy to improve and coordinate planning across jurisdictions, provider types, and the public and private sectors. Guided by a pan-Canadian advisory committee, our project synthesized evidence and refined key priorities through a virtual policy dialogue. This article describes the insights generated at this dialogue and highlights the coordinated priority actions for a MHSUH workforce strategy for Canada. Specific actions are recommended under the following five priorities: (1) collect data for planning; (2) support the workforce; (3) target recruitment; (4) optimize and diversify roles; and (5) close policy gaps. This proposed strategy can inform effective workforce planning, foster the well-being of the MHSUH workforce, and facilitate retention and recruitment. Engagement from MHSUH system partners, including leaders from government, provider, and lived experience organizations, is essential to advancing this workforce strategy.
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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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".