Is time really of the essence? Timeliness of narrative feedback in ophthalmology CBME assessments
Bibliographic record
Abstract
PURPOSE: Competency-based medical education relies on a strong program of assessment, and quality comments play a vital role in ensuring its success. The goal of this study is to determine the effect of the timeliness of assessment completion on the quality of the feedback. MATERIALS AND METHODS: Using the Quality of Assessment for Learning (QuAL) score 2478 assessments were evaluated. The assessments included those completed between July 2017 and December 2020 for 18 ophthalmology residents. Spearman correlation, Mann-Whitney U and Kruskal-Wallis tests were used to assess variations in QuAL scores based on the timeliness of assessment completion. RESULTS: The timeliness of assessment completion ranged from 0 to 299 d with the mean time for completion being 3 d. As the delay increased, the QuAL score decreased. Feedback provided 4, 5, and 14 d post-encounter demonstrated statistically significant differences in the QuAL score. Additionally, there was a significant difference in the timeliness of feedback when there is no written comment. CONCLUSIONS: This study demonstrates that the timeliness of assessment completion might have an effect on the quality of written feedback. Written feedback should be completed within 14 d of the encounter to optimize quantity and quality.
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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 | 0.027 | 0.278 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".