Ontario’s health profession regulatory landscape: a mixed-methods study of structures, practices, and perceptions
Bibliographic record
Abstract
BACKGROUND: Health profession regulatory colleges in Canada are comprised of public and professional members working collaboratively to regulate a profession and protect the public. Though this general structure has inhered for decades, British Columbia recently amalgamated several colleges into larger agencies that represent multiple professions. This study investigates the landscape of health profession regulation in Ontario with the aim of guiding policymakers, professionals, and the public as they navigate both existing structures and potential changes. METHODS: Senior-level staff from each of the 26 colleges in Ontario (regulating 30 distinct professions) were invited to participate in a 28-question online survey and semi-structured interviews in 2022. The survey and interviews explored structures, practices, and perceptions within Ontario's health profession regulatory bodies. RESULTS: Forty-six survey responses were received, with participants representing 22/26 health profession regulators in Ontario. 5 of these participants were engaged in follow-up interviews. Results showed that Ontario colleges range in size, with a mean of 47.4 staff and 16.3 board members (53.8% professional, 46.2% public), that they regulate an average of 8994.6 health professionals, and that they maintain a variety of committees. They receive a minimum of 5 complaints annually and a maximum of 550, with a minimum of 0 and maximum of 25 leading to disciplinary action. Complaints range from sexual assault (< 10% of total complaints) to breach of standards of practice (> 50%). Policy infrastructure is consistent amongst colleges, with most maintaining policies for code of conduct (94.7%) and fewer for speaking and writing engagements (23.7%). Finally, participants felt their colleges were "very effective" in public protection (62.9%), "very ethical" in their activities (68.6%), and with "very functional or functional" (88.8%) councils. 94.2% indicated that a mixture of professional and public members should run councils (the status quo) and 62.9% agreed with the current model of self-regulation. CONCLUSIONS: Health profession regulatory systems benefit from a current picture of existing regulatory bodies. Input and perceptions from college staff-individuals embedded in the everyday operation of these institutions-should be considered to guide potential changes to the structure of health profession regulation in Ontario.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".