Use the right words: evaluating the effect of word choice and word count on quality of narrative feedback in ophthalmology competency-based medical education assessments
Bibliographic record
Abstract
Background: The purpose of this study was to investigate the effect of word choice on the quality of narrative feedback in ophthalmology resident trainee assessments following the introduction of competency-based medical education at Queen’s University. Methods: Assessment data from July 2017-December 2020 were retrieved from ElentraTM (Integrated Teaching and Learning Platform) and anonymized. Written feedback was assigned a Quality of Assessment for Learning (QuAL) score out of five based on this previously validated rubric. The correlation between QuAL score and specific coaching words was determined using a Spearman’s Rho analysis. Independent samples t-tests were used to compare the QuAL score when a specific word was used, and when it was absent. Results: A total of 1997 individual assessments were used in this analysis. The number of times the identified coaching words were used within a comment was significantly and positively associated with the total QuAL score, with the exception of “next time” (rho=0.039, p=0.082), “read” (rho = 0.036, p = 0.112), “read more” (rho = -0.025, p = 0.256) and “review” (rho = -0.017, p = 0.440). The strongest correlations were for “continue” (rho = 0.182, p < 0.001), “try(ing)” (rho = 0.113, p < 0.001) and “next step” (rho = 0.103, p < 0.001). The mean value of the QuAL score increased when coaching words were used vs. not used with the largest mean difference of 1.44 (p < 0.001) for “reflect”. A clear positive relationship was demonstrated between word count and QuAL score (rho = .556, p < 0.001). Conclusions: The use of certain coaching words in written comments may improve the quality of feedback.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".