The sum of the parts detracts from the intended whole: competencies and in‐training assessments
Bibliographic record
Abstract
OBJECTIVES: Despite the fact that Canadian residency programmes are required to assess trainees' performance within the context of the CanMEDS Roles Framework, there has been no inquiry into the potential relationship between residents' perceptions of the framework and their in-training assessments (ITA). Using data collected during the study of ITA, we explored residents' perceptions of these competencies. METHODS: From May 2006-07, a purposive sample of 20 resident doctors from internal medicine, paediatrics, and surgery were interviewed about their ITA experiences. Data collection and analysis proceeded in an iterative fashion consistent with grounded theory. In April 2008, a summary of recurrent themes was presented during a focus group interview of another five residents to afford further elaboration and refinement of thematic findings. RESULTS: The in-training assessment report (ITAR) was perceived as a primary source of residents' information on CanMEDS. Residents' familiarity with the set of competencies appeared to be quite limited and they possessed narrow definitions of the roles. Several trainees questioned the framework's relevance and some appeared confused about the overlapping nature of the roles. Although residents viewed the central Medical Expert role as the most relevant and important competency, they incorrectly perceived it as only involving the acquisition of medical and scientific knowledge. A visual rhetorical analysis of a typical ITAR suggests that the visual features found within this assessment tool may be misrepresenting the framework and the centrality of the Medical Expert role. CONCLUSIONS: Resident doctors' knowledge of CanMEDS was found to be limited. The visual structure of the ITAR appears to be a factor in residents' apparent distortion of the CanMEDS construct from its original holistic philosophy.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".