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Record W3082065223 · doi:10.1080/14999013.2020.1812772

Voluntary and Forced Migrants in Forensic Mental Health Care

2020· article· en· W3082065223 on OpenAlexaffabout
Stephanie R. Penney, Aaron Prosser, Teresa Grimbos, Egag Egag, Alexander I. F. Simpson

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster UniversityUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMental healthMental illnessPsychiatryForensic scienceMedicinePopulationCriminal justiceHealth careDemographyPsychologyEnvironmental healthCriminologySociologyPolitical science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.354
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes2
Has abstractyes

Explore more

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