Exploring Police Officer Experiences During the Transfer of People in Crisis to Emergency Department Nurses and Staff
Bibliographic record
Abstract
Police officers are increasingly tasked with responding to people in crisis (PIC), often resulting in transfers to emergency departments (EDs) where they can encounter challenges like long wait times, safety concerns, and conflicting perspectives between the medical and legal systems. This qualitative study explores the experiences of police officers during the transfer of PIC to ED nurses and staff. Eleven police officers were recruited and interviewed, providing contextual information about ED transfers in the greater Montreal area. The findings revealed that police officers face varied challenges in transferring PIC to different EDs, influenced by each hospital's unique characteristics, staffing, security, and organizational issues. Relationships with nurses significantly impact these experiences, ranging from positive interactions to tension and conflict influenced by staff biases and differing perceptions of the PIC. Effective communication between police and ED nurses is critical for accurate clinical evaluation and decision-making, yet often hindered by inconsistent information transfer and procedural gaps. Role confusion, divergent philosophies between police officers and ED nurses, and 'grey zones' further complicate transfers, emphasizing the need for clear communication and mutual understanding to ensure safe and effective care. The results underscore the need to ameliorate ED transfers through enhanced joint training for police officers and nurses, establishing hospital-precinct committees, and other intersectoral initiatives to promote collaboration. Such measures are essential to ensure effective and compassionate care of people in crisis while prioritizing safety for all involved.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| 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".