Defining Health Profession Regulators’ Roles in the Canadian Healthcare System
Bibliographic record
Abstract
Health professions regulation today faces a myriad of challenges, due to both the perceived performance of regulatory colleges, how health systems have evolved, and even larger political and economic shifts such as the renegotiation of NAFTA. In this issue of Healthcare Papers, Wilkie and Tzountzouris (2017) describe the work of the College of Medical Laboratory Technologists of Ontario (CMLTO) to redefine professionalism in the context of these challenges. Their paper, and the comments of the responding authors in this issue highlight that there, is an overarching perception that health regulatory structures - across a range of professions - are not working as effectively as they should. Across this issue of Healthcare Papers, attention is drawn to the fact that more can be done to improve both the function and perception of professional regulatory bodies. However, each paper presents a different approach to how improvements in function and perception are possible.
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 | 0.055 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.064 | 0.049 |
| Scholarly communication | 0.042 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".