Patient-on-Staff Assaults: Perspectives of Mental Health Staff at an Acute Inpatient Psychiatric Teaching Hospital in the United States
Bibliographic record
Abstract
INTRODUCTION: Physical assaults perpetrated by patients in psychiatric hospitals against mental health staff (MHS) is a serious concern facing psychiatric hospitals. Assaulted staff reports physical and psychological trauma that affects their personal and professional lives. There is a dearth of literature exploring this phenomenon. PURPOSE: To explore MHS perspectives of assault by psychiatric patients. METHODS: A transcendental phenomenological qualitative design was used to explore and analyze the perspectives of a purposeful sample of 120 MHS perspectives at an acute inpatient psychiatric hospital. Participants' age ranged from 22 to 63 years (mean age = 32.4). Moustakas' theoretical underpinnings guided the study. RESULTS: Two patterns, 8 themes, and 19 subthemes were identified: (a) Psychological impacts revealed four themes-increase of anxiety/fear level, helplessness and hopelessness, flashbacks/burnout, and doubting own competency. (b) Physiosocial impacts revealed four themes-unsupportive superiors, stigmatization of staff victim, failure to report the incident, and environmental safety. DISCUSSION: Participants verbalized that assaults by patients have instilled fear and trauma in them. Most of the assaults occurred when staff were performing their routine job functions and setting limits to patient's behavior. CONCLUSION: The study allowed MHS opportunities to narrate their lived experiences of being assaulted by patients and provided validation of their perspectives. Findings illuminated the phenomenon and may help to support policy changes in psychiatric hospitals.
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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 | 0.002 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".