A battle for attention: how do emergency physicians respond to interruptions? A scoping review
Bibliographic record
Abstract
OBJECTIVES: Managing constant interruptions is an intrinsic aspect of emergency medicine practice; however, effective physician responses to mitigate their impact remain unclear. Although interruptions are exceedingly common and closely linked to adverse patient outcomes, the approaches for managing such disruptions have yet to be established. In this study, we aimed to identify how emergency physicians and residents respond to interruptions and the factors influencing their responses. METHODS: We conducted a scoping review to identify how emergency physicians and residents manage interruptions in the emergency department, searching the following databases using controlled vocabulary: Medline, APA PsycInfo, SCOPUS, and PubMed. Data extraction was conducted using the Covidence platform through a standardized and structured process. RESULTS: The scoping review identified 18 relevant articles, the majority of which (12/18) employed observational methods, involving a total of 417 emergency physicians. These observations, amounting to 778.6 h, were conducted across 25 emergency departments in 4 countries. Physicians responded to interruptions through task switching, multitasking, deferral, acknowledgment, and rejection. Their responses were influenced by the nature of the interrupted activity, cognitive load management, use of telecommunications, physicians' perceptions, and the work environment. CONCLUSION: Emergency departments are inherently fast-paced and prone to interruptions; therefore, it is important to better understand how emergency physicians and residents navigate these disruptions. This study explores how physicians respond to interruptions and provides insights to support clinicians in identifying and developing effective management strategies.
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".