Multisource Feedback and Self-Assessment of the Communicator, Collaborator, and Professional CanMEDS Roles for Diagnostic Radiology Residents
Bibliographic record
Abstract
PURPOSE: To develop a tool for the external and self-evaluation of residents in the Communicator, Collaborator, and Professional CanMEDS roles. METHODS: An academic teaching institution affiliated with 4 major urban hospitals conducted a survey that involved 46 residents and 216 hospital staff members. Residents selected at least 13 external evaluators from different categories (including physicians, nurses or technologists, peers or fellows, and support staff members) from their last 6 months of rotations. The external evaluators and residents answered 4 questions that pertained to each of the 3 CanMEDS roles being assessed. The survey results were analysed for feasibility, variance within and between rater groups, and the relationships between multisource and self-evaluation scores, and between multisource feedback and in-training evaluation report scores. RESULTS: The multisource feedback survey had an overall response rate of 73% with 683 evaluations sent out to 216 unique evaluators. The ratings from different groups of evaluators were only weakly correlated. Residents were most likely to receive their best rating from a collaborating physician and their worst rating from a site secretary or a program assistant. Generally, self-assessment scores were significantly lower than multisource feedback scores. Although there was a strong correlation within the multisource feedback data and within the in-training evaluation report data, there was a weak correlation among the data sets. CONCLUSIONS: Multisource feedback provides useful feedback and scores that relate to critical CanMEDS roles that are not necessarily reflected in a resident's in-training evaluation report. The self-assessment feature of multisource feedback permits a resident to compare the accuracy of his or her assessments to improve their life-long learning skills.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".