Validity and Utility of the CanMEDS “Resident as Teacher Multisource Feedback” Assessment Tool for Resident-led Structured Teaching
Bibliographic record
Abstract
We evaluated the validity of using the CanMEDS Resident as Teacher Multisource Feedback (RaTMSF) assessment tool to gather learner feedback from structured resident-led teaching within the University of Toronto's postgraduate geriatric medicine residency program. The RaTMSF consists of 10 rated items and narrative comments. Completed RaTMSF evaluations from resident teachers were analyzed by descriptive statistics for internal consistency and inter-rater reliability, and narrative comments were reviewed for thematic content. Resident teachers were surveyed on the acceptability of the tool to develop teaching competencies. A total of 132 evaluations were collected prospectively from 11 residents from April 2021 to April 2022, and retrospectively from seven graduates from 2016 to 2019. The overall performance rating, 4.75 (SD 0.47), was very positive for all resident teachers. The RaTMSF demonstrated high internal consistency with Cronbach's alpha of 0.97, 95% CI 0.89-0.99 between all 10 items, and good inter-rater reliability with Fleiss kappa of 0.73 (95% CI 0.13-0.80). The most common themes of narrative comments also captured in the rated items were organization to teach (n=53) and openness to questions (n=36). Written comments regarding delivery style (n=52) and audience interactivity (n=44) were not captured on the rated items. While most resident teachers surveyed found the RaTMSF acceptable to use, we suggest opportunities to improve the RaTMSF by restructuring focus onto written feedback and revising rated items to better reflect themes found in narrative comments. The RaTMSF can be a valuable feedback tool to help residents gather high-quality feedback on their teaching 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 | 0.073 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".