Registered Nurses’ Experiences of Patient Violence on Acute Care Psychiatric Inpatient Units
Bibliographic record
Abstract
Nurses working in acute care psychiatry experience high rates of violence perpetrated by patients and their perspectives on these experiences are essential to understand this phenomenon. The purpose of this study was to explore psychiatric nurses’ experiences of patient violence in acute care inpatient psychiatric settings. In this interpretive descriptive study, a purposeful sample of 12 nurses were interviewed to understand how they define patient violence and understand their experiences of abuse within the workplace. Using thematic content analysis, a problem, needs and practice analysis was also conducted. Experiencing patient violence had many perceived negative impacts on nurses, patients and the organization. It was often considered to be part of the job and some nurses struggled with the role conflict between one’s duty to care and one’s duty to self when needing to provide care following a critical incident. Power, control and stigma also influenced nurses’ perceptions and responses to patient violence. In their practice, nurses used a wide variety of interventions to stay safe as well as prevent and manage patient violence. Nurses recommended increased education, support and debriefing, and an improved working environment. Future research should explore a consistent definition of violence, barriers to incident reporting and the creation of best practice guidelines specifically related to patient violence. Understanding the perspectives and experiences of nurses in acute inpatient psychiatry leads to greater knowledge of the phenomenon of patient violence and helps to inform the development of future nursing interventions to prevent and to respond to patient violence, as well as support nurses working within the acute care setting.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".