An online Delphi study to investigate the completeness of the CanMEDS Roles and the relevance, formulation, and measurability of their key competencies within eight healthcare disciplines in Flanders
Bibliographic record
Abstract
BACKGROUND: Several competency frameworks are being developed to support competency-based education (CBE). In medical education, extensive literature exists about validated competency frameworks for example, the CanMEDS competency framework. In contrast, comparable literature is limited in nursing, midwifery, and allied health disciplines. Therefore, this study aims to investigate (1) the completeness of the CanMEDS Roles, and (2) the relevance, formulation, and measurability of the CanMEDS key competencies in nursing, midwifery, and allied health disciplines. If the competency framework is validated in different educational programs, opportunities to support CBE and interprofessional education/collaboration can be created. METHODS: A three-round online Delphi study was conducted with respectively 42, 37, and 35 experts rating the Roles (n = 7) and key competencies (n = 27). These experts came from non-university healthcare disciplines in Flanders (Belgium): audiology, dental hygiene, midwifery, nursing, occupational therapy, podiatry, and speech therapy. Experts answered with yes/no (Roles) or on a Likert-type scale (key competencies). Agreement percentages were analyzed quantitatively whereby consensus was attained when 70% or more of the experts scored positively. In round one, experts could also add remarks which were qualitatively analyzed using inductive content analysis. RESULTS: After round one, there was consensus about the completeness of all the Roles, the relevance of 25, the formulation of 24, and the measurability of eight key competencies. Afterwards, key competencies were clarified or modified based on experts' remarks by adding context-specific information and acknowledging the developmental aspect of key competencies. After round two, no additional key competencies were validated for the relevance criterion, two additional key competencies were validated for the formulation criterion, and 16 additional key competencies were validated for the measurability criterion. After adding enabling competencies in round three, consensus was reached about the measurability of one additional key competency resulting in the validation of the complete CanMEDS competency framework except for the measurability of two key competencies. CONCLUSIONS: The CanMEDS competency framework can be seen as a grounding for competency-based healthcare education. Future research could build on the findings and focus on validating the enabling competencies in nursing, midwifery, and allied health disciplines possibly improving the measurability of key competencies.
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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 | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".