Bibliographic record
Abstract
Nurses are at an increased risk for violence from patients compared to other healthcare professionals working in hospital emergency departments. In this setting, there are multiple factors contributing to patient violence including long wait-times, overcrowding, insufficient staffing, and lack of security personnel. This violence can be verbal, physical, or sexual in nature, and may result in psychological, emotional, cognitive, and social consequences. While there is an abundance of literature that explores how nurses working in the emergency department experience patient violence, less is known about how patient violence affects their day-to-day nursing practice. The purpose of this study was to explore how nurses working in Ontario emergency departments who have experienced patient violence enact their nursing care. This was an interpretive description qualitative study using semi-structured, conversation-style telephone interviews, set in Ontario, Canada. Data were analyzed using conventional content analysis. The participants’ experiences with patient violence and its effect on their nursing care were described using four categories and seven subcategories: ‘Violent Context’ (Leadership), (Wait-Times), (Security Measures), ‘Being Responsible’ (Work Family), ‘Violent Patients’ (Patient for Whom we Anticipate Violence), (Patients who Surprise Us), and ‘Adapting their Practice’ (Engaging with Patients). Nurses working in the emergency department describe frequent occurances of physical and verbal violence as part of their daily practice. This violence leads to emotional and psychological consequences, as well as changes to their nursing care and interactions with future patients. Inconsistencies in hospital policies, resources, and supports create an environment where nurses are often left to manage both the violent encounter and their personal and professional responses.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".