Emergency Medicine Clerkship Encounter and Procedure Logging Using Handheld Computers
Bibliographic record
Abstract
BackgroundTracking medical student clinical encounters is now an accreditation requirement of medical schools. The use of handheld computers for electronic logging is emerging as a strategy to achieve this. ObjectivesTo evaluate the technical feasibility and student satisfaction of a novel electronic logging and feedback program using handheld computers in the emergency department. MethodsThis was a survey study of fourth-year medical student satisfaction with the use of their handheld computers for electronic logging of patient encounters and procedures. The authors also included an analysis of this technology. ResultsForty-six students participated in this pilot project, logging a total of 2,930 encounters. Students used the logs an average of 7.6 shifts per rotation, logging an average of 8.3 patients per shift. Twenty-nine students (63%) responded to the survey. Students generally found it easy to complete each encounter (69%) and easy to synchronize their handheld computer with the central server (83%). However, half the students (49%) never viewed the feedback Web site and most (79%) never reviewed their logs with their preceptors. Overall, only 17% found the logging program beneficial as a learning tool. ConclusionsElectronic logging by medical students during their emergency medicine clerkship has many potential benefits as a method to document clinical encounters and procedures performed. However, this study demonstrated poor compliance and dissatisfaction with the process. In order for electronic logging using handheld computers to be a beneficial educational tool for both learners and educators, obstacles to effective implementation need to be addressed.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".