Learning in the ED: chaos, partners and paradoxes
Bibliographic record
Abstract
Purpose Most contemporary research in medical education focuses on the undergraduate component conducted within medical schools. The purpose of this paper, however, is to better understand how medical residents and practicing attending physicians learned to practice within the context of the emergency medicine department (ED) workplace. Design/methodology/approach In all, 18 residents and 15 attending physicians were interviewed about their learning in the ED. Interviews were digitally recorded and transcribed verbatim then analysed using an iterative approach. Emergent themes were shared with the participants to ensure they were an accurate representation of their lived experiences. Findings The first of the three main findings was that the ED learning environment was characterised as “messy” because of the inherently chaotic nature of the workplace. The second finding was that patients and nurses were informal partners in learning. The third main finding was that learning and working in the ED can be difficult, isolating and often lacks continuity. Research limitations/implications The main limitation associated with this research relates to the highly situated and contextually bound nature of this study. Nevertheless, the findings should be generative for others interested in supporting the work and learning of health professionals. Originality/value This study shifts the focus in medical education research from formal undergraduate education to learning in high stress and chaotic workplaces. Accordingly, this work provides valuable insights for others interested in the messy realities of learning in professional practice.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".