The Quality of Assessment for Learning score for evaluating written feedback in anesthesiology postgraduate medical education: a generalizability and decision study
Bibliographic record
Abstract
Background: Competency based residency programs depend on high quality feedback from the assessment of entrustable professional activities (EPA). The Quality of Assessment for Learning (QuAL) score is a tool developed to rate the quality of narrative comments in workplace-based assessments; it has validity evidence for scoring the quality of narrative feedback provided to emergency medicine residents, but it is unknown whether the QuAL score is reliable in the assessment of narrative feedback in other postgraduate programs. Methods: Fifty sets of EPA narratives from a single academic year at our competency based medical education post-graduate anesthesia program were selected by stratified sampling within defined parameters [e.g. resident gender and stage of training, assessor gender, Competency By Design training level, and word count (≥17 or <17 words)]. Two competency committee members and two medical students rated the quality of narrative feedback using a utility score and QuAL score. We used Kendall's tau-b co-efficient to compare the perceived utility of the written feedback to the quality assessed with the QuAL score. The authors used generalizability and decision studies to estimate the reliability and generalizability coefficients. Results: < 0.001) were moderately correlated. Results from the generalizability studies showed that utility scores were reliable with two raters for both faculty (Epsilon=0.87, Phi=0.86) and trainees (Epsilon=0.88, Phi=0.88). Conclusions: The QuAL score is correlated with faculty- and trainee-rated utility of anesthesia EPA feedback. Both faculty and trainees can reliability apply the QuAL score to anesthesia EPA narrative feedback. This tool has the potential to be used for faculty development and program evaluation in Competency Based Medical Education. Other programs could consider replicating our study in their specialty.
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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 | 0.018 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".