Epidemiology of competence: a scoping review to understand the risks and supports to competence of four health professions
Bibliographic record
Abstract
OBJECTIVES: This study examined the risks and supports to competence discussed in the literature related to occupational therapists, pharmacists, physical therapists and physicians, using epidemiology as a conceptual model. DESIGN: Articles from a scoping literature review, published from 1975 to 2014 inclusive, were included if they were about a risk or support to the professional or clinical competence of one of four health professions. Descriptive and regression analyses identified potential associations between risks and supports to competence and the location of study, type of health profession, competence life-cycle and the domain(s) of competence (organised around the CanMEDS framework). RESULTS: A total of 3572 abstracts were reviewed and 943 articles analysed. Most focused on physicians (n=810, 86.0%) and 'practice' (n=642, 68.0%). Fewer articles discussed risks to competence (n=418, 44.3%) than supports (n=750, 79.5%). The top four risks, each discussed in over 15% of articles, were: transitions in practice, being an international graduate, lack of clinical exposure/experience (ie, insufficient volume of procedures or patients) and age. The top two supports (over 35%) were continuing education participation and educational information/programme features. About 60% of all the articles discussed medical expert and about 25% applied to all roles. Articles focusing on residents had a greater probability of reporting on risks. CONCLUSIONS: Articles about physicians were dominant. The majority of articles were written in the last decade and more discussed supports than risks to competence. An epidemiology-based conceptual model offers a helpful organising framework for exploring and explaining the competence of health 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 | 0.027 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.049 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".