Quality Assurance and Maintenance of Competence Assessment Mechanisms in the Professions:
Bibliographic record
Abstract
Regulatory bodies of health and non-health professions around the world have developed a diverse array of mechanisms to ensure maintenance of competence of practitioners. Quality assurance of professionals' practices is crucial to the work of regulators, yet there are few examples of interprofessional or cross-jurisdictional comparisons of approaches and mechanisms used to achieve this important objective. This review was undertaken using an indicative sampling method: to control for local cultural factors, all regulated health- and non-health professions in a single jurisdiction (Ontario, Canada) were studied, while intra-jurisdictional comparison was facilitated through targeted study of large professions (such as medicine, pharmacy and teaching) in other English-language jurisdictions (such as California, USA; the United Kingdom and Australia). A total of 91 regulated professions were examined to identify trends, commonalities and differences related to approaches used for professional quality assurance and maintenance of competence assessment. A diverse array of approaches was identified, highlighting divergent approaches to defining and measuring competency in the professions. Further comparative work examining this issue is required to help identify best- and promising-practices that can be shared among regulators from different jurisdictions and professions.
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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 | 0.075 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".