Prompting better feedback: investigating the effect of targeted form design on quality of narrative feedback in ophthalmology CBME assessments
Bibliographic record
Abstract
OBJECTIVE: Competency-based medical education (CBME) requires a variety of assessments evaluating resident performance. Assessment form design may influence narrative feedback quality. To evaluate the effect of including targeted written comment prompts in entrustable professional activity (EPA) assessment forms on the quality of narrative feedback in CBME ophthalmology resident trainee assessments. METHODS: Transition to discipline (TTD) assessment data from three distinct time periods were anonymized; the first 2 groups contained assessments completed with the original form design, whereas the last group represented assessments completed after the introduction of revised EPA forms. Written feedback was scored using the Quality of Assessment for Learning (QuAL) score. One-way ANOVA and a Tukey post hoc test were used to compare mean QuAL scores of each group. RESULTS: One-thousand one-hundred and forty-five assessments were analyzed, including 680 Original EPA forms, 322 intermediate forms, and 143 revised forms. QuAL scores significantly increased after revisions were made to the assessment form, with original, intermediate, and revised form mean QuAL scores of 2.14 ± 1.76, 2.77 ± 1.75, and 4.33 ± 1.11; P < 0.001 for all comparisons, respectively. CONCLUSIONS: Revising EPA form design to include targeted prompts and examples of evidence-based coaching words to guide written comments results in higher-quality narrative feedback in CBME assessments.
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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 | 0.076 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".