Patient Handover as a Learning Activity for Medical Students
Bibliographic record
Abstract
The move of medical education in North America to a model based on teaching competencies rather than knowledge requires new methods of assessment. As part of a longer-term project to create objective and quantifiable measures for the learning of these competencies, we argue that the focus should be on the Entrustable Professional Activities (EPAs) that are associated with each competency. In this paper we present a theoretical constructivist analysis of one such EPA, that of patient handover, which is critical to patient safety. By applying an Activity Theory analysis to this EPA, we were able to identify, and thus quantify, key components of the EPA. Analysis of student performance on an online virtual patient simulation of a trauma case indicated that surgical clerkship students were able to correctly mention the majority of the procedures that expert surgeons indicated were most important, but only used less than a third of the professional terms that experts thought appropriate. This result points to a need to increase direct instruction in professional communication among medical learners, while demonstrating that Activity Theory provides an analysis that not only captures what is happening in the learning, but also can be used to develop objective and quantifiable assessment metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".