Validity evidence for the Quality of Assessment for Learning score: a quality metric for supervisor comments in Competency Based Medical Education
Bibliographic record
Abstract
Background: Competency based medical education (CBME) relies on supervisor narrative comments contained within entrustable professional activities (EPA) for programmatic assessment, but the quality of these supervisor comments is unassessed. There is validity evidence supporting the QuAL (Quality of Assessment for Learning) score for rating the usefulness of short narrative comments in direct observation. Objective: We sought to establish validity evidence for the QuAL score to rate the quality of supervisor narrative comments contained within an EPA by surveying the key end-users of EPA narrative comments: residents, academic advisors, and competence committee members. Methods: In 2020, the authors randomly selected 52 de-identified narrative comments from two emergency medicine EPA databases using purposeful sampling. Six collaborators (two residents, two academic advisors, and two competence committee members) were recruited from each of four EM Residency Programs (Saskatchewan, McMaster, Ottawa, and Calgary) to rate these comments with a utility score and the QuAL score. Correlation between utility and QuAL score were calculated using Pearson's correlation coefficient. Sources of variance and reliability were calculated using a generalizability study. Results: = 0.68). The generalizability study found that the major source of variance was the comment indicating the tool performs well across raters. Conclusion: The QuAL score may serve as an outcome measure for program evaluation of supervisors, and as a resource for faculty development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.236 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".