Pan-Canadian Registration and Licensure of Health Professionals: A Path Forward Emerging from a Best Brains Exchange Policy Dialogue
Bibliographic record
Abstract
The regulation of health professions differs across Canadian provinces and territories, often resulting in an unstandardized approach to licensure and registration. These siloed regulatory frameworks hinder health workforce mobility and virtual care - with implications for patient safety and equitable access to healthcare - and pose a barrier to integrated health workforce planning. The authors report on a Best Brains Exchange policy dialogue held in October 2019 on pan-Canadian registration and licensure (CIHR 2019), highlighting leading practices and presenting a potential path forward through pan-Canadian regulatory mechanisms. Situating these findings within the context of the COVID-19 pandemic demonstrates the urgency for governments to move on this reform.
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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 | 0.046 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.027 |
| Scholarly communication | 0.030 | 0.012 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.027 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 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".