Learner handover: Perspectives and recommendations from the front-line
Bibliographic record
Abstract
INTRODUCTION: Current medical education models increasingly rely on longitudinal assessments to document learner progress over time. This longitudinal focus has re-kindled discussion regarding learner handover-where assessments are shared across supervisors, rotations, and educational phases, to support learner growth and ease transitions. The authors explored the opinions of, experiences with, and recommendations for successful implementation of learner handover among clinical supervisors. METHODS: Clinical supervisors from five postgraduate medical education programs at one institution completed an online questionnaire exploring their views regarding learner handover, specifically: potential benefits, risks, and suggestions for implementation. Survey items included open-ended and numerical responses. The authors used an inductive content analysis approach to analyze the open-ended questionnaire responses, and descriptive and correlational analyses for numerical data. RESULTS: Seventy-two participants completed the questionnaire. Their perspectives varied widely. Suggested benefits of learner handover included tailored learning, improved assessments, and enhanced patient safety. The main reported risk was the potential for learner handover to bias supervisors' perceptions of learners, thereby affecting the validity of future assessments and influencing the learner's educational opportunities and well-being. Participants' suggestions for implementation focused on who should be involved, when and for whom it should occur, and the content that should be shared. DISCUSSION: The diverse opinions of, and recommendations for, learner handover highlight the necessity for handover to maximize learning potential while minimizing potential harms. Supervisors' suggestions for handover implementation reveal tensions between assessment-of and for-learning.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| 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".