Processes of In-Hospital Psychiatric Care and Subsequent Criminal Behaviour among Patients with Schizophrenia: A National Population-Based, Follow-up Study
Bibliographic record
Abstract
OBJECTIVES: It is unknown whether evidence-based, in-hospital processes of care may influence the risk of criminal behaviour among patients with schizophrenia. Our study aimed to examine the association between guideline recommended in-hospital psychiatric care and criminal behaviour among patients with schizophrenia. METHODS: Danish patients with schizophrenia (18 years or older) discharged from a psychiatric ward between January 2004 and March 2009 were identified using a national population-based schizophrenia registry (n = 10 757). Data for in-hospital care and patient characteristics were linked with data on criminal charges obtained from the Danish Crime Registry until November 2010. RESULTS: Twenty per cent (n = 2175) of patients were charged with a crime during follow-up (median = 428 days). Violent crimes accounted for 59% (n = 1282) of the criminal offences. The lowest risk of crime was found among patients receiving the most processes of in-hospital care (top quartile of received recommended care, compared with bottom quartiles, adjusted hazard ratio = 0.86, 95% CI 0.75 to 0.99). The individual processes of care associated with the lowest risk of criminal behaviour were antipsychotic treatment and staff contact with relatives. CONCLUSIONS: High-quality, in-hospital psychiatric care was associated with a lower risk of criminal behaviour after discharge among patients with schizophrenia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".