Nudging clinical supervisors to provide better in-training assessment reports
Bibliographic record
Abstract
INTRODUCTION: In-training assessment reports (ITARs) summarize assessment during a clinical placement to inform decision-making and provide formal feedback to learners. Faculty development is an effective but resource-intensive means of improving the quality of completed ITARs. We examined whether the quality of completed ITARs could be improved by 'nudges' from the format of ITAR forms. METHODS: Our first intervention consisted of placing the section for narrative comments at the beginning of the form, and using prompts for recommendations (Do more, Keep doing, Do less, Stop doing). In a second intervention, we provided a hyperlink to a detailed assessment rubric and shortened the checklist section. We analyzed a sample of 360 de-identified completed ITARs from six disciplines across the three academic years where the different versions of the ITAR were used. Two raters independently scored the ITARs using the Completed Clinical Evaluation Report Rating (CCERR) scale. We tested for differences between versions of the ITAR forms using a one-way ANOVA for the total CCERR score, and MANOVA for the nine CCERR item scores. RESULTS: Changes to the form structure (nudges) improved the quality of information generated as measured by the CCERR instrument, from a total score of 18.0/45 (SD 2.6) to 18.9/45 (SD 3.1) and 18.8/45 (SD 2.6), p = 0.04. Specifically, comments were more balanced, more detailed, and more actionable compared with the original ITAR. DISCUSSION: Nudge interventions, which are inexpensive and feasible, should be included in multipronged approaches to improve the quality of assessment reports.
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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 | 0.073 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".