Coaching for Chaos: A Qualitative Study of Instructional Methods for Multipatient Management in the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Busy environments, like the emergency department (ED), require teachers to develop instructional strategies for coaching trainees to function within these same environments. Few studies have documented the strategies used by emergency physician (EP)-teachers within these busy, chaotic environments, instead emphasizing teaching in more predictable environments such as the outpatient clinic, hospital wards, or operating room. The authors sought to discover what strategies EP-teachers were using and what trainees recalled experiencing when learning to handle these unpredictable, overcrowded, complex, multipatient environments. METHOD: An interpretive description study was conducted at multiple teaching hospitals affiliated with McMaster University from July 2014 to May 2015. Participants (10 EP-teachers and 10 junior residents) were asked to recall teaching strategies related to handling ED patient flow. Participants were asked to describe techniques that they used, observed, or experienced as trainees. Two independent coders read through interview transcripts, analyzing these documents inductively and iteratively. RESULTS: Two main types of strategies to teach ED management were discovered: 1) workplace-based methods, including both observation and in situ instruction; and 2) principle-based advice. The most often described techniques were workplace-based methods, which included a variety of in situ techniques ranging from conversations to managerial coaching (e.g., collaborative problem-solving of real-life administrative dilemmas). CONCLUSIONS: A mix of strategies are used to teach and coach trainees to handle multipatient environments. Further research is required to determine how to optimize the use of these techniques and innovate new strategies to support the learning of these crucial skills.
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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 | 0.041 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".