Characteristics of alleged homicide offenders with and without schizophrenia in Sichuan, China
Bibliographic record
Abstract
BACKGROUND: Little is known about the characteristics of people with and without schizophrenia who have been charged with homicide in China. AIMS AND RESEARCH QUESTION: Our research question was what differences are there between alleged homicide offenders with and without psychosis? METHOD: All archival records of alleged homicide cases referred for assessment to the West China Forensic Central Medical Service during 1998-2006 were retrieved. The centre serves a large catchment area in the mainly rural province of Sichuan. A random 20% of cases with schizophrenia and all cases without psychosis were selected for comparison. Demographic, criminological and mental health data were extracted from the records, and violence was rated by using the Violence Risk Scale (Chinese version). RESULTS: The two groups differed significantly in age, education, occupation, marital status and relationships to victim. The estimated risk of reoffending was higher in the schizophrenia group than the non-psychotic group, even after controlling for demographic differences. Despite many individuals reporting long histories of mental illness, about 40% of those with schizophrenia had never had any psychiatric treatment and less than 4% were in treatment at the time of the alleged homicide. CONCLUSIONS: The tendency for homicidal people with schizophrenia to be older, less educated and more socially isolated than their non-psychotic peers is similar to experience in Western countries, but the apparently higher risk scale scores of the Chinese schizophrenia group and their greater tendency to attack strangers are different. The lack of reported previous engagement with mental health services by a clearly ill and risky group of people is a likely explanation. Similar rural problems compared with better served urban areas have been reported in the Chuvash Republic. The case for better rural mental health services seems strong. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".