The development of an instrument to assess clinical teaching with linkage to CanMEDS roles: A psychometric analysis
Bibliographic record
Abstract
BACKGROUND: Assessment of clinical teaching by learners is of value to teachers, department heads, and program directors, and must be comprehensive and feasible. AIMS: To review published evaluation instruments with psychometric evaluations and to develop and psychometrically evaluate an instrument for assessing clinical teaching with linkages to the CanMEDS roles. METHOD: We developed a 19-item questionnaire to reflect 10 domains relevant to teaching and the CanMEDS roles. A total of 317 medical learners assessed 170 instructors. Fourteen (4.4 %) clinical clerks, 229 (72.3%) residents, and 53 (16.7%) fellows assessed 170 instructors. Twenty-one (6.6%) did not specify their position. RESULTS: A mean number of eight raters assessed each instructor. The internal consistency reliability of the 19-item instrument was Cronbach's α = 0.95. The generalizability coefficient (Ep(2)) analysis indicated that the raters achieved Ep(2) of 0.95. The factor analysis showed three factors that accounted for 67.97% of the total variance. The three factors together, with the variance accounted for and their internal consistency reliability, are teaching skills (variance = 53.25s%; Cronbach's α = 0.92), Patient interaction (variance = 8.56%; Cronbach's α = 0.91), and professionalism (variance = 6.16%; Cronbach's α = 0.86). The three factors are intercorrelated (correlations = 0.48, 0.58, 0.46; p < 0.01). CONCLUSION: It is feasible to assess clinical teaching with the 19-item instrument that has demonstrated evidence of both validity and reliability.
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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 | 0.053 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 0.001 |
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".