Aggression in Acute Inpatient Psychiatric Care: A Survey of Staff Attitudes
Bibliographic record
Abstract
Introduction Inpatient aggression poses consistent complications for psychiatric hospitals. It can affect patient and staff safety, morale, and quality of care. Research on staff attitudes toward patient aggression is sparse. Purpose The study explored staff attitudes toward patient aggression by hospital position types and years of experience in a psychiatric hospital. We predicted that staff experiencing patient aggression would be related to working in less trained positions, having less psychiatric work experience, and demonstrating attitudes that were consistent with attributes internal to the patient and not external. Methods Fifty-one percent completed online survey using Management of Aggression and Violence Attitude Scale, along with demographics, years of work experience, and number of times staff experienced aggressive event. Results Management of Aggression and Violence Attitude Scale scores, staff position types, and years of experience were related to the number of aggressive interactions. Nurses and psychiatric technicians reported highest number of exposures to patient aggression, followed by physicians; however, support staff reported less patient aggression. More years worked in a psychiatric hospital was associated with more aggressive experience. Conclusion Nurses, psychiatric technicians, and physicians reported greater exposure to patients’ aggression than support staff. Training programs, developed specifically to individual position types, focusing on recognition of sources of aggression, integrated into staff training, might reduce patient on staff aggression in psychiatric hospitals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".