Professional Governance Systems that Work: Managing risks and ensuring regulatory effectiveness in a global and digital age.
Bibliographic record
Abstract
Many health professionals are governed by regulatory bodies, established through legislation to ensure that these workers practice competently and safely to serve the needs of their patients/clients and communities, and ultimately contribute to overall public health and societal well-being. As our healthcare systems struggle in the post-pandemic era to meet the high demand for healthcare services, policymakers in Canada, the United Kingdom (UK) and other countries have sought solutions that frequently have implications for the healthcare professional workforce. These solutions include greater reliance on internationally educated healthcare workers and increased adoption of artificial intelligence (AI) and related technologies to increase workforce productivity. Identifying leading governance practices in these areas to inform policymaking, however, is a challenge, and it is not yet clear how regulatory effectiveness can be determined. This knowledge synthesis project seeks to inform policymaking in both Canada and the UK by exploring what regulatory risks are raised by international migration of healthcare practitioners and digital technologies, and how these risks can be addressed in an effective manner that builds public confidence in professional regulation. Goals and Objectives This knowledge synthesis will examine research and policy to inform policymaking in both Canada and the UK, guided by our collaborators in the field of professional regulation. This project is aligned with the overarching funding call focus on envisioning governance systems that work, and also several sub-themes including digital governance, health governance, and international governance. Our core objectives are to answer the following research questions: (1) What regulatory risks are raised by international migration of healthcare practitioners and digitization? (2) Are these risks relevant to everyone or for some groups more than others? (3) How can regulatory effectiveness in these areas be identified, demonstrated and measured? As a research domain that will undoubtedly become more complex and diverse in the coming decade, it is essential to generate a synthesized understanding of the governance systems in professional regulation about technological change and internationalization. Policymakers and regulators are currently exploring regulatory solutions in these areas, making this a governance priority. To address our research questions, we will undertake a scoping review. Scoping reviews are applicable when examining the extent, range, and nature of evidence in a given domain, especially when it is difficult to visualize the range of evidence that might be available. To operationally guide our knowledge synthesis, we will use the six-step scoping review framework articulated by Arksey and O’Malley and further described by Levac, Colquhoun, and O’Brien, as well as the corresponding guidance from the Joanna Briggs Institute Reviewer’s Manual.
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 | 0.123 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.075 |
| Scholarly communication | 0.034 | 0.029 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".