Medical and Undergraduate Student Perceptions on Scribing in an Emergency Department
Bibliographic record
Abstract
Background A shift towards electronic medical records (EMR) has increased physician burnout and decreased physician satisfaction and productivity. One solution to alleviate EMR stressors is the implementation of medical scribes. Scribes have been shown to increase physician productivity and satisfaction. The study objective was to elucidate medical and undergraduate student scribing experience to determine if that experience can incentivize scribes to work in the emergency department. Methods Ten students scribed and shadowed at a tertiary ED between July 4, 2019, and August 10, 2019. Medical students participated in two scribing and two non-scribing (shadowing) sessions, each lasting four hours. Undergraduate students only had a scribing condition. To facilitate scribing, a laptop with a wireless keyboard was provided, as well as a stand-up laptop tray. An exit survey and semi-structured interviews were conducted after the scribing experience. The majority of insights were extracted from interviews. Transcripts were coded into thematic coding trees and analyzed using thematic analysis. Results All undergraduate students preferred volunteering in the ED over other volunteer experiences. All undergraduates cited direct access to the medical field, resume building, and perceived value added to the health care team as motivators to continue scribing. Most students credited demystification of the medical profession as a motivator. Most medical students felt scribing should be integrated into their curriculum. Based on survey results, five undergraduate students would volunteer 40 hours/week. Conclusion Our study showed that a volunteer model of scribing is feasible. Importantly, scribing may be an invaluable experience for directing career goals and ensuring that students intrinsically interested in medicine pursue the profession. Although a volunteer model may not provide the desired benefit in terms of ED efficiency, it may be an integral part of training the next wave of physicians.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".