Legislating interprofessional collaboration: A policy analysis of health professions regulatory legislation in Ontario, Canada
Bibliographic record
Abstract
Changes to Ontario's health professions regulatory system were initiated through various legislative amendments. These amendments introduced a legislative obligation for health regulatory colleges to support interprofessional collaboration (IPC), collaborate where they share controlled acts, and incorporate IPC into their quality assurance programs. The purpose of this policy analysis was to identify activities, strategies, and collaborations taking place within health professions regulatory colleges pertaining to legislative changes related to IPC. A qualitative content analysis of (1) college documents pertaining to IPC (n = 355) and (2) interviews with representatives from 14 colleges. Three themes were identified: ideal versus reality; barriers to the ideal; and legislating IPC. Commitment to the ideal of IPC was evident in college documents and interviews. Colleges expressed concern about the lack of clarity regarding the intent of legislation. In addition, barriers stemming from long-standing issues in practice including scope of practice protection, conflicting legislation, and lack of knowledge about the roles of other health professionals impede IPC. Government legislation and health professional regulation have important roles in supporting IPC; however, broader collaboration may be required to achieve policy objectives.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.038 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".