Drivers of regulatory reform in Canadian health professions: Institutional isomorphism in a shifting social context
Bibliographic record
Abstract
Abstract Research has documented how the decline in professional self-regulation in the UK and Australia was led by policy-makers in response to regulatory failures. In Canada, professional self-regulation is currently in decline as well, and while policy-makers have driven some change it is also the case that self-regulating professions have begun to transform themselves from within: altering their structure, make-up, and processes to enhance fairness, public input, and accountability, while reducing professional control. Why would they do so? This paper draws on the concept of institutional isomorphism to understand why professional regulators would invoke changes that, on the surface, might seem to counteract their own interests. Analysing data from 46 interviews with leaders in healthcare profession regulation, this paper examines how coercive, mimetic, and normative processes drive regulatory reform in a changing regulatory field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".