Regulating Health Professional Scopes of Practice: Comparing Institutional Arrangements & Approaches in the US, Canada, Australia & the UK
Bibliographic record
Abstract
Abstract BackgroundFundamentally, the goal of health professional regulatory regimes is to ensure the highest quality of care to the public. Part of that task is to control what health professionals do, or their scope of practice. Ideally, this involves the application of evidence-based professional standards of practice to the tasks for which health professional have received training. There are different jurisdictional approaches to achieving these goals. MethodsUsing a comparative case study approach and similar systems policy analysis design, we present and discuss four different regulatory approaches from the US, Canada, Australia and the UK. For each case, we highlight the jurisdictional differences in how these countries regulate health professional scopes of practice in the interest of the public. Our comparative Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis is based on archival research carried out by the authors wherein we describe the evolution of the institutional arrangements for form of regulatory approach, with specific reference to scope of practice.Results/ConclusionsOur comparative examination finds that the different regulatory approaches in these countries have emerged in response to similar challenges. In some cases, ‘tasks’ or ‘activities’ are the basis of regulation, whereas in other contexts protected ‘titles’ are regulated, and in some cases both. We discuss the implications for how these different approaches achieve positive outcomes for the public but also for health professionals and the system more broadly in terms of workforce optimization.
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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 | 0.039 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".