The McMaster Narrative Comment Rating Tool: Development and Initial Validity Evidence
Bibliographic record
Abstract
CONSTRUCT: The McMaster Narrative Comment Rating Tool aims to capture critical features reflecting the quality of written narrative comments provided in the medical education context: valence/tone of language, degree of correction versus reinforcement, specificity, actionability, and overall usefulness. BACKGROUND: Despite their role in competency-based medical education, not all narrative comments contribute meaningfully to the development of learners' competence. To develop solutions to mitigate this problem, robust measures of narrative comment quality are needed. While some tools exist, most were created in specialty-specific contexts, have focused on one or two features of feedback, or have focused on faculty perceptions of feedback, excluding learners from the validation process. In this study, we aimed to develop a detailed, broadly applicable narrative comment quality assessment tool that drew upon features of high-quality assessment and feedback and could be used by a variety of raters to inform future research, including applications related to automated analysis of narrative comment quality. APPROACH: In Phase 1, we used the literature to identify five critical features of feedback. We then developed rating scales for each of the features, and collected 670 competency-based assessments completed by first-year surgical residents in the first six-weeks of training. Residents were from nine different programs at a Canadian institution. In Phase 2, we randomly selected 50 assessments with written feedback from the dataset. Two education researchers used the scale to independently score the written comments and refine the rating tool. In Phase 3, 10 raters, including two medical education researchers, two medical students, two residents, two clinical faculty members, and two laypersons from the community, used the tool to independently and blindly rate written comments from another 50 randomly selected assessments from the dataset. We compared scores between and across rater pairs to assess reliability. FINDINGS: <.05), apart from valence, which was only significantly correlated with degree of correction versus reinforcement. CONCLUSION: Our findings suggest that the McMaster Narrative Comment Rating Tool can reliably be used by multiple raters, across a variety of rater types, and in different surgical contexts. As such, it has the potential to support faculty development initiatives on assessment and feedback, and may be used as a tool to conduct research on different assessment strategies, including automated analysis of narrative comments.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".