Part IV: Assessing and Managing Violent Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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 it