Part IV: Assessing and Managing Violent Patients
Bibliographic record
Abstract
offered the opportunity to contribute to a section discuss-ing experience with special patient populations. An im-portant area of forensic psychiatric research pertains to research on violence and aggressive behaviours. Violence is a broad concept that may include verbal threats and psychological and physical aggression. Numerous re-searchers have come to the conclusion that there is a defi-nite relation between violence and mental illness (1,2). In a large, community-based epidemiological survey (n = 10,000), Swanson and others found that an Axis I diagno-sis increased the risk of violent behaviour 10 to 15 times for substance use disorders and five to six times for the anxiety, affective and schizophrenic disorders (2). Several studies have found that psychosis and schizophrenia are associated with violent acts against others, including homicide (3–6). Psychiatrists often encounter violence in acute care hospi-tal settings, emergency departments and outpatient serv-ices. Faulker and others reviewed the survey literature pertaining to threats and assaults on psychiatrists and con-ducted their own survey of Oregon psychiatrists. They concluded that assaults and threats were frequent, oc-curred across various settings and involved a wide range of patients. The psychiatrists ’ sex was not a factor (7). In the Canadian context, Chaimowitz and Moscovitch sur-veyed all psychiatric residents who were members of the
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.027 |
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".