The Prediction of Violence in Acute Psychiatric Units
Bibliographic record
Abstract
This is a prospective longitudinal study addressing the issue of predicting violence within two weeks of admission to two psychiatric units. Predictors of violence in 100 consecutive admissions were collected at baseline and tested on an outcome of violence. Risk factors were analyzed using logistic regression models and cross-validated using jack-knifing techniques. Two separate definitions of violence were used, actual assault on another person and aggressive behavior including attempted assault, violence to property and specific threats. Thirty-two patients actually assaulted, 41 behaved aggressively and 27 were completely non-violent. Two models were developed to predict membership according to these definitions. A single model based on a three-category outcome compared unfavorably with the two-model approach. Although the presence of recent pre-admission violence had some predictive value for both models, clinical variables were most predictive. “Non-violence” and “violence” appeared to be two separate states, and did not fit neatly on to a continuum. The two models were statistically robust, but should be tested prospectively on larger samples.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".