A Novel Model of Patient Handover in Emergency Medicine—Addressing Hidden Tensions in Culture
Bibliographic record
Abstract
Introduction: Clinical handover (or handoff), the transfer of patient care between providers, is essential in Emergency Medicine. Poor communication during handover can threaten patient safety. Existing literature views handover as an information transfer; little research has examined the human interaction within handover and how it affects patient safety. We sought a more nuanced understanding of handover to improve this critical aspect of Emergency Medicine care. Methods: We used constructivist grounded theory to explore handover as a social phenomenon. We invited staff emergency physicians in an academic, tertiary care hospital to participate in semi-structured interviews, which were recorded, de-identified, and transcribed. The research team analyzed the transcripts in multiple progressively interpretive analytical stages: initial, focused, and theoretical. Cultural-Historical Activity Theory (CHAT) provided a framework for our data analysis. CHAT situates an individual's action into an activity system in which components interact, producing tensions. We sought to attain theoretical sufficiency by revising the interview guide and recruiting participants for specific insights. Results: We interviewed sixteen (16) participants. During handover, the object was for the incoming physician (IP) to accept the handover plan from the outgoing physician (OP). We identified a rule requiring minimal handover plans that were realistic and dichotomous. The divisions of labor were tasks. During the interaction, emotional reactions such as judgment or compassion, and behavioral reactions such as pushback or acceptance could have resulted. There were unintended consequences from the handover process that impacted members' self-worth and generated threats to patient safety. Conclusions: We built a novel and more nuanced framework of handover in the emergency department that involves multiple interactions and tensions. Using this new model, emergency departments should adjust their handover processes to enhance group dynamics and patient safety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".