Does Educational Handover Influence Subsequent Assessment?
Bibliographic record
Abstract
PURPOSE: Educational handover (i.e., providing information about learners' past performance) is controversial. Proponents argue handover could help tailor learning opportunities. Opponents fear it could bias subsequent assessments and lead to self-fulfilling prophecies. This study examined whether raters provided with reports describing learners' minor weaknesses would generate different assessment scores or narrative comments than those who did not receive such reports. METHOD: In this 2018 mixed-methods, randomized, controlled, experimental study, clinical supervisors from 5 postgraduate (residency) programs were randomized into 3 groups receiving no educational handover (control), educational handover describing weaknesses in medical expertise, and educational handover describing weaknesses in communication. All participants watched the same videos of 2 simulated resident-patient encounters and assessed performance using a shortened mini-clinical evaluation exercise form. The authors compared mean scores, percentages of negative comments, comments focusing on medical expertise, and comments focusing on communication across experimental groups using analyses of variance. They examined potential moderating effects of supervisor experience, gender, and mindsets (fixed vs growth). RESULTS: Seventy-two supervisors participated. There was no effect of handover report on assessment scores (F(2, 69) = 0.31, P = .74) or percentage of negative comments (F(2, 60) = 0.33, P = .72). Participants who received a report indicating weaknesses in communication generated a higher percentage of comments on communication than the control group (63% vs 50%, P = .03). Participants who received a report indicating weaknesses in medical expertise generated a similar percentage of comments on expertise compared to the controls (46% vs 47%, P = .98). CONCLUSIONS: This study provides initial empirical data about the effects of educational handover and suggests it can-in some circumstances-lead to more targeted feedback without influencing scores. Further studies are required to examine the influence of reports for a variety of performance levels, areas of weakness, and learners.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".