Aggressive behaviors in the psychiatric emergency service
Bibliographic record
Abstract
INTRODUCTION: Studies of aggressive behaviors in a nonforensic mental health setting have focused primarily on the inpatient ward and, on event prediction, using behavior-based clinical rating scales. Few studies have specifically targeted aggressive behaviors in the psychiatric emergency service or determined whether assessing the demographic and clinical characteristics of such patients might prove useful for their more rapid identification. METHODS: We used a prospectively acquired database of over 20,900 visits to four services in the province of Quebec, Canada, over a two-year period from September 2002 onwards. A maximum of 72 variables could be acquired per visit. Visits with aggression (any verbally or physically intimidating behavior), both present and past, were tagged. Binary logistic regressions and cross-tabulations were used to determine whether the profile of a variable differed in visits with aggression from those without aggression. RESULTS: About 7% of visits were marked by current aggression (verbal 49%, physical 12%, verbal and physical 39%). Including visits with a "past only" history of aggression increased this number to 20%. Variables associated with aggression were gender (male), marital status (single/separated), education (high school or less), employment (none), judicial history (any type), substance abuse (prior or active), medication compliance (poor), type of arrival to psychiatric emergency services (involuntary, police, judiciary, landlord), reason for referral (behavioral dyscontrol), diagnosis (less frequent in anxiety disorders), and outcome (more frequently placed under observation or admitted). CONCLUSION: Our results suggest that many state-independent variables are associated with aggressive behaviors in the psychiatric emergency service. Although their sum may not add up to a specific patient profile, they can nevertheless be useful in service planning, being easily integrated alongside state-dependent rating scales in a triage and/or observation instrument for daily use in the psychiatric emergency service.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".