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Record W2022580223 · doi:10.1097/nmd.0b013e3181beac52

Use of Integrated Dual Disorder Treatment Via Assertive Community Treatment Versus Clinical Case Management for Persons With Co-Occurring Disorders and Antisocial Personality Disorder

2009· article· en· W2022580223 on OpenAlexaff
Linda K. Frisman, Kim T. Mueser, Nancy H. Covell, Hsiu‐Ju Lin, Anne G. Crocker, Robert E. Drake, Susan M. Essock

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2009
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersNational Institute of Mental HealthNational Institute on Alcohol Abuse and Alcoholism
KeywordsAntisocial personality disorderDual diagnosisAssertive community treatmentPsychiatryPsychologyAlcohol use disorderAssertivenessMental illnessPersonality disordersSubstance abuseClinical psychologyPersonalityMedicineAlcoholMental healthPoison controlInjury preventionPsychotherapistEnvironmental health

Abstract

fetched live from OpenAlex

We conducted secondary analyses of data from a randomized trial testing the effectiveness of Assertive Community Treatment (ACT) in delivery of integrated dual disorder treatment (IDDT) to explore the impact of IDDT delivered through ACT teams compared with standard clinical case management for dually-disordered persons with and without antisocial personality disorder (ASPD). This analysis included 36 individuals with ASPD and 88 individuals without ASPD. Participants with ASPD assigned to ACT showed a significantly greater reduction in alcohol use and were less likely to go to jail than those in standard clinical case management, whereas participants without ASPD did not differ between the 2 case management approaches. There were no significant differences for other substance use or criminal justice outcomes. This study provides preliminary evidence that persons with co-occurring serious mental illness, substance use disorders, and ASPD may benefit from delivery of IDDT through ACT teams.

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)0.001
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 score0.414
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.082
GPT teacher head0.399
Teacher spread0.317 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

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