Failure to flow: An exploration of learning and teaching in busy, multi-patient environments using an interpretive description method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: As patient volumes continue to increase, more attention must be paid to skills that foster efficiency without sacrificing patient safety. The emergency department is a fertile ground for examining leadership and management skills, especially those that concern prioritization in multi-patient environments. We sought to understand the needs of emergency physicians (EPs) and emergency medicine junior trainees with regards to teaching and learning about how best to handle busy, multi-patient environments. METHOD: A cognitive task analysis was undertaken, using a qualitative approach to elicit knowledge of EPs and residents about handling busy emergency department situations. Ten experienced EPs and 10 junior emergency medicine residents were interviewed about their experiences in busy emergency departments. Transcripts of the interviews were analyzed inductively and iteratively by two independent coders using an interpretive description technique. RESULTS: EP teachers and junior residents differed in their perceptions of what makes an emergency department busy. Moreover, they focused on different aspects of patient care that contributed to their busyness: EP teachers tended to focus on volume of patients, junior residents tended to focus on the complexity of certain cases. The most important barrier to effective teaching and learning of managerial skills was thought to be the lack of faculty development in this skill set. CONCLUSIONS: This study presents qualitative data that helps us elucidate how patient volumes affect our learning environments, and how clinical teachers and residents operate within these environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it