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Record W1971682494 · doi:10.1002/bsl.827

Community‐Based co‐occurring disorder (COD) intermediate and advanced treatment for offenders

2008· article· en· W1971682494 on OpenAlexaff
Gerald Melnick, Carrie Coen, Faye S. Taxman, Stanley Sacks, Katherine M. Zinsser

Bibliographic record

VenueBehavioral Sciences & the Law · 2008
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
FundersNational Institute on Drug Abuse
KeywordsTypologyPrisonMental healthPsychiatrySubstance abuseCriminal justiceEmpirical evidenceSubstance abuse treatmentRecidivismMultidisciplinary approachPsychologyCriminologySociologySocial science

Abstract

fetched live from OpenAlex

Against a backdrop of increasing concern about the adequacy of treatment for co-occurring substance use and mental disorders (typically known as "co-occurring disorders," or COD) in the criminal justice system, this article attempts to provide empirical evidence for a typology of levels of COD treatment for offenders in both prison and community settings. The paper investigates two levels of treatment programs for COD; "intermediate" programs, in which treatment programming has been designed primarily for offenders with a single disorder, and "advanced" programs, in which programming has been designed to provide integrated substance abuse treatment and mental health services. Findings from a national survey of program directors indicated that both intermediate and advanced COD treatment programs were similar in their general approach to substance abuse treatment, but differed considerably in their treatment of mental disorders, where the advanced programs employed significantly more evidence- and consensus-based practices. Results provide support for the distinction between intermediate- and advanced-level services for offenders with COD and support a typology that defines advanced programs as integrating a range of evidence- and consensus-based practices so as to modify treatment sufficiently to address both diseases.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.902

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 studies0.0010.001
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 head0.198
GPT teacher head0.418
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

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

Citations15
Published2008
Admission routes1
Has abstractyes

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