Fitness-for-purpose of the CanMEDS competencies for workplace-based assessment in General Practitioner’s Training: a Delphi study
Bibliographic record
Abstract
BACKGROUND: In view of the exponential use of the CanMEDS framework along with the lack of rigorous evidence about its applicability in workplace-based medical trainings, further exploring is necessary before accepting the framework as accurate and reliable competency outcomes for postgraduate medical trainings. Therefore, this study investigated whether the CanMEDS key competencies could be used, first, as outcome measures for assessing trainees' competence in the workplace, and second, as consistent outcome measures across different training settings and phases in a postgraduate General Practitioner's (GP) Training. METHODS: In a three-round web-based Delphi study, a panel of experts (n = 25-43) was asked to rate on a 5-point Likert scale whether the CanMEDS key competencies were feasible for workplace-based assessment, and whether they could be consistently assessed across different training settings and phases. Comments on each CanMEDS key competency were encouraged. Descriptive statistics of the ratings were calculated, while content analysis was used to analyse panellists' comments. RESULTS: Out of twenty-seven CanMEDS key competencies, consensus was not reached on six competencies for feasibility of assessment in the workplace, and on eleven for consistency of assessment across training settings and phases. Regarding feasibility, three out of four key competencies under the role "Leader", one out of two competencies under the role "Health Advocate", one out of four competencies under the role "Scholar", and one out of four competencies under the role "Professional" were deemed as not feasible for assessment in a workplace setting. Regarding consistency, consensus was not achieved for one out of five competencies under "Medical Expert", two out of five competencies under "Communicator",one out of three competencies under "Collaborator", one out of two under "Health Advocate", one out of four competencies under "Scholar", one out of four competencies under "Professional". No competency under the role "Leader" was deemed to be consistently assessed across training settings and phases. CONCLUSIONS: The findings indicate a mismatch between the initial intent of the CanMEDS framework and its applicability in the context of workplace-based assessment. Although the CanMEDS framework could offer starting points, further contextualization of the framework is required before implementing in workplace-based postgraduate medical trainings.
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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 | 0.100 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".