Aggression in psychiatric hospitalizations: a qualitative study of patient and provider perspectives
Bibliographic record
Abstract
BACKGROUND: When the people hospitalized in psychiatric units demonstrate aggression, it harms individuals and creates legal and financial issues for hospitals. Aggression has been linked to inpatient, clinician and environmental characteristics. However, previous work primarily accessed clinicians' perspectives or administrative data and rarely incorporated inpatients' insights. This limits validity of findings and impedes comparisons of inpatient and clinician perspectives. AIMS: This study explored and compared inpatient and clinician perspectives on the factors affecting verbal and physical aggression by psychiatric inpatients. METHODS: This study used an interpretive theoretical framework. Fourteen inpatients and 10 clinicians were purposefully sampled and completed semi-structured interviews. Data were analyzed using inductive thematic analysis. RESULTS: Six themes were identified at personal and organizational levels. The three person-level themes were major life stressors, experience of illness and interpersonal connections with clinicians. The three organization-level themes were physical confinement, behavioural restrictions and disengagement from treatment decisions. CONCLUSIONS: Aggression is perceived to have a wide range of origins spanning personal experiences and organizational policies, suggesting that a wide range of prevention strategies are needed.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".