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Record W1984785370 · doi:10.1037/h0093964

Treating offenders with mental illness: A research synthesis.

2011· review· en· W1984785370 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLaw and Human Behavior · 2011
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton UniversityYork University
FundersNational Institute of Mental Health
KeywordsRecidivismMental illnessPsychological interventionPsychologyCriminal justicePsychiatryMental healthEmpirical researchLegal psychologyPopulationClinical psychologyMedicineSocial psychologyCriminology

Abstract

fetched live from OpenAlex

The purpose of this research synthesis was to examine treatment effects across studies of the service providers to offenders with mental illness. Meta-analytic techniques were applied to 26 empirical studies obtained from a review of 12,154 research documents. Outcomes of interest in this review included measures of both psychiatric and criminal functioning. Although meta-analytic results are based on a small sample of available studies, results suggest interventions with offenders with mental illness effectively reduced symptoms of distress, improving offender's ability to cope with their problems, and resulted in improved behavioral markers including institutional adjustment and behavioral functioning. Furthermore, interventions specifically designed to meet the psychiatric and criminal justice needs of offenders with mental illness have shown to produce significant reductions in psychiatric and criminal recidivism. Finally, this review highlighted admission policies and treatment strategies (e.g., use of homework), which produced the most positive benefits. Results of this research synthesis are directly relevant for service providers in both criminal justice and mental health systems (e.g., psychiatric hospitals) as well as community settings by informing treatment strategies for the first time, which are based on empirical evidence. In addition, the implications of these results to policy makers tasked with the responsibility of designating services for this special needs population are highlighted.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.231
GPT teacher head0.452
Teacher spread0.221 · 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