Geographic Risk Management: A Spatial Study of Mentally Disordered Offenders Discharged from Forensic Psychiatric Care
Bibliographic record
Abstract
We investigated the impact of neighborhood and community factors on the reintegration of forensic patients leaving custodial care in British Columbia, Canada. Using geographic information systems (GIS) techniques, the residential locations of a sample of forensic patients were tracked over time and mapped in relation to each other. The frequency of a patient's return to hospital was monitored and the reasons for these returns were recorded after each unsuccessful community placement. The analysis of the findings suggested that patients who were released to certain, socially disorganized neighborhoods returned to inpatient care at a higher frequency. These neighborhoods exhibited many destabilizing features that may have significant influence on the long-term success or failure of discharge patients, such as low income, high unemployment, poor educational achievement, and concentrated rental accommodation. Further research is needed in order to explore not only the influence of neighbourhood destabilizers on the length of community placements for forensic mental health patients, but also the underlying rationale for locating patient services in socially disorganized areas.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".