Involuntary Outpatient Commitment and the Elusive Pursuit of Violence Prevention
Bibliographic record
Abstract
OBJECTIVE: Involuntary outpatient commitment (OPC)-also referred to as 'assisted outpatient treatment' or 'community treatment orders'-are civil court orders whereby persons with serious mental illness and repeated hospitalisations are ordered to adhere to community-based treatment. Increasingly, in the United States, OPC is promoted to policy makers as a means to prevent violence committed by persons with mental illness. This article reviews the background and context for promotion of OPC for violence prevention and the empirical evidence for the use of OPC for this goal. METHOD: Relevant publications were identified for review in PubMed, Ovid Medline, PsycINFO, personal communications, and relevant Internet searches of advocacy and policy-related publications. RESULTS: Most research on OPC has focussed on outcomes such as community functioning and hospital recidivism and not on interpersonal violence. As a result, research on violence towards others has been limited but suggests that low-level acts of interpersonal violence such as minor, noninjurious altercations without weapon use and arrests can be reduced by OPC, but there is no evidence that OPC can reduce major acts of violence resulting in injury or weapon use. The impact of OPC on major violence, including mass shootings, is difficult to assess because of their low base rates. CONCLUSIONS: Effective implementation of OPC, when combined with intensive community services and applied for an adequate duration to take effect, can improve treatment adherence and related outcomes, but its promise as an effective means to reduce serious acts of violence is unknown.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".