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 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 | 0.062 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".