Static and Dynamic Variables Associated with Inpatient Aggression: A Two-Year Retrospective Study: Variables statiques et dynamiques associées au comportement agressif des patients hospitalisés : étude rétrospective de deux ans
Bibliographic record
Abstract
BackgroundAggressive behaviour is common in mental health inpatient units, and can cause physical and psychological harm, low work satisfaction among staff and be disruptive to the clinical care of patients. Identification of static and dynamic variables associated with inpatient aggression may help identify opportunities for intervention to reduce such incidents.MethodWe carried out a two-year retrospective study of consecutive admissions to the Centre for Addiction and Mental Health, the largest mental health facility in Canada. We created a multivariable model of risk factors associated with aggression, which included static and dynamic variables, as well as the Dynamic Appraisal of Situational Aggression (DASA), which was measured daily.ResultsWe included 4419 consecutive admissions comprising 88,124 patient-days. We found that High and Medium DASA scores were strongly associated with subsequent aggression (HR = 9.64, 95% CI = 7.75-11.99, and HR = 3.51, 95% CI = 2.82-4.37, respectively) after controlling for other variables. Other variables associated with aggression included the Aggressive Behaviour Scale of the Resident Assessment Instrument-Mental Health (RAI-ABS), male gender, younger age, ethnicity, PRN (as needed medication) administration, unit type, involuntary admission, medication refusal and self-harm. However, these variables were more weakly associated with subsequent aggression as compared to the DASA score categories.ConclusionsHigher DASA scores are strongly associated with aggression after controlling for a range of other patient variables. Frequent structured measurement of dynamic variables using the DASA may help identify patients most at risk of aggression and assist clinical staff in directing interventions to where they are most needed to reduce aggression on inpatient units.Plain Language Summary TitleWhat Factors Are Linked to Aggression in Mental Health Hospitals? A Two-Year Study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".