Voluntary and Forced Migrants in Forensic Mental Health Care
Bibliographic record
Abstract
Foreign-born migrants are at elevated risk for developing serious forms of mental illness, and are over-represented in criminal justice and forensic mental health settings. This study compares the clinical characteristics of forced ( n = 60) and voluntary ( n = 226) migrant patients in a Canadian forensic service to native-born patients ( n = 234), and contrasts regions of birth represented in the current sample to the total adult migrant population in our catchment city ( N = 2,537,410). Compared to Canadian-born patients, migrant patients were more likely to have a later-onset psychotic disorder as their only diagnosis, and less likely to have a personality disorder diagnosis. Migrant patients had more familial supports prior to illness onset, but were socially isolated near the time of forensic admission. Compared to the total adult migrant population, higher proportions of Caribbean, Central American, Eastern and Western African persons were represented in the forensic system, while fewer individuals from East and South Asia were represented. There was no effect of migrancy status on the duration of forensic hospitalization or community supervision. Findings suggest disproportionate minority representation among users of forensic services, and highlight what may be ineffective pathways to adequate mental health care among certain migrant groups.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".