Out from the shadows: What health leaders should do to advance the mental health and substance use health workforce
Bibliographic record
Abstract
The Mental Health and Substance Use Health (MHSUH) impacts of the COVID-19 pandemic are proving to be significant, complex, and long-lasting. The MHSUH workforce-including psychologists, social workers, psychotherapists, addiction counsellors, and peer support workers as well as psychiatrists, family physicians, and nurses-is the backbone of the response. As health leaders consider how to address long-standing and emerging health workforce challenges, there is an opportunity to move the MHSUH workforce out from the shadows through full inclusion in health workforce planning in Canada. After first examining the roots and consequences of the long-standing exclusion of the MHSUH workforce, this paper presents findings from a recent study showing how the pandemic has compounded MHSUH workforce capacity issues. Priorities for MHSUH workforce action by health leaders include closing regulation gaps, engaging the public and private sectors in coordinated planning, and accelerating data collection through a central health workforce registry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".