The ‘missing person’ in roles‐based competency models: a historical, cross‐national, contrastive case study
Bibliographic record
Abstract
CONTEXT: The use of roles such as medical expert, advocate or communicator to define competencies is currently popular in health professions education. CanMEDS is one framework that has been subject to great uptake across multiple countries and professions. The examination of the historical and cultural choices of names for roles generates insight into the nature and construction of roles. One role that has appeared in and disappeared from roles-based frameworks is that of the 'person'. METHODS: In order to examine the implications of explicitly including or excluding the role of the 'physician as person' in a competency framework, we conducted a contrastive analysis of the development of frameworks in Canada and the Netherlands. We drew upon critical social science theoretical understandings of the power of language in our analysis. RESULTS: In Canada, the 'person' role was a late addition to the precursory work that informed CanMEDS, and was then excluded from the final set of CanMEDS role names. In the Netherlands, a 'reflector' role was added in some Dutch schools and programmes when CanMEDS was adopted. This was done in order to explicitly emphasise the importance of the 'person' of the trainee. CONCLUSIONS: In analysing choices of names for roles, we have the opportunity to see how cultural and historical contexts affect conceptions of the roles of doctors. The taking up and discarding of the 'person' role in Canada and the Netherlands suggest that as medical educators we may need to further consider the ways in which we wish the trainee as a person to be made visible in the curriculum and in assessment tools.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".