Quality Assurance and Maintenance of Competence Assessment Mechanisms in the Professions:
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it