Understanding, teaching and assessing the elements of the CanMEDS Professional Role: Canadian Program Directors’ views
Bibliographic record
Abstract
BACKGROUND: Physicians are required to maintain and sustain professional roles during their careers, making the Professional Role an important component of postgraduate education. Despite this, this role remains difficult to define, teach and assess. OBJECTIVE: To (a) understand what program directors felt were key elements of the CanMEDS Professional Role and (b) identify the teaching and assessment methods they used. METHODS: A two-step sequential mixed method design using a survey and semi-structured interviews with Canadian program directors. RESULTS: Forty-six program directors (48% response rate) completed the questionnaire and 10 participated in interviews. Participants rated integrity and honesty as the most important elements of the Role (96%) but most difficult to teach. There was a lack of congruence between elements perceived to be most important and most frequently taught. Role modeling was the most common way of informally teaching professionalism (98%). Assessments were most often through direct feedback from faculty (98%) and feedback from other health professionals and residents (61%). Portfolios (24%) were the least used form of assessment, but they allowed residents to reflect and stimulated self-assessment. CONCLUSION: Program directors believe elements of the Role are difficult to teach and assess. Providing faculty with skills for teaching/assessing the Role and evaluating effectiveness in changing attitudes/behaviors should be a priority in postgraduate programs.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".