Bibliographic record
Abstract
This paper will identify existing models of professional regulatory reform that could ensure a single licensing process that results in multi-jurisdictional licensure. The paper will also distinguish models or ad hoc arrangements that fail to achieve such a result. This is a timely topic in Canada because its professional regulatory framework is being examined for modernization.The Canadian professional regulatory framework for health care providers is a legacy of the Constitution of 1867. It can be characterized as a Federation of 13 different jurisdictional systems with each province or territory having exclusive jurisdiction over regulation of its health professionals. This results in differing entry to practice requirements, standards of practice, classes or categories of registration and transfer criteria for eligibility from other provinces.The United States nursing state board regulators, the Australian Commonwealth, and their state governments have moved from their original regulatory frameworks to modern ones. Their models are more supportive of mobility, cross-border virtual care, education, and health provider professional development and well-being. Aside from recent discussions in the 4 Canadian Atlantic provinces, there has been little will, effort, or advancement to modernize the regulatory framework in Canada to support multi-jurisdictional licensure. This paper aims to briefly describe 6 existing models that support multijurisdictional licensure. In the fall of 2022, the 4 Atlantic Premiers (akin to state Governors in the US) asked each of their medical regulatory authority (akin to State Medical Boards) to develop a licensing system such that physicians could practice in all 4 Atlantic provinces without the need to acquire multiple licenses. Two models will be discussed that meet this recently stated objective of the Atlantic Premiers while the others do not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".