Pan-Canadian Licensure: Without Having to Change the Constitution
Bibliographic record
Abstract
The regulatory system in Canada for self-regulated health care professions is like a propeller plane with as many propellers as there are provincial/territorial regulatory colleges/councils for the particular profession. The challenge with having more than one propeller, is that they all rotate at different speeds and sometimes not even in the same direction. This makes for a bumpy, noisy, and long ride. Should one of the propellers require repair or maintenance the whole plane has to return to the jurisdiction from which the propeller originated — not particularly climate sensitive or efficient. The impact of this regulatory framework for regulated health care providers is that providers can only practice in the province/territory (“PT”) in which they are licensed. If providers wish to practice in another PT, they must apply and present documentation to that PT and pay applicable fees. The desire for timely access to health care that is supported by technology are expectations of the public, especially with the COVID-19 pandemic having made obvious the inequities and gaps in our health system. The regulatory solution is ideally pan-Canadian licensure so that providers may work across the country under one license. Pan-Canadian licensure is a multi-faceted challenge which usually begins and ends with a comment that “it cannot be done” because it would require constitutional change. This article focuses on the legal framework of how to achieve pan-Canadian licensure without having to change the constitution and sets out a proposed regulatory-legal model, inspired by the Australian model and the Canadian securities models. There are myriad of other issues that would need addressing to implement pan-Canadian licensure, but these are not the subject of this article.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".