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Record W2024960560 · doi:10.1080/14999013.2014.890682

Can Antisocial Personality Disorder Be Treated? A Meta-Analysis Examining the Effectiveness of Treatment in Reducing Recidivism for Individuals Diagnosed with ASPD

2014· article· en· W2024960560 on OpenAlexaff
Holly A. Wilson

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRecidivismAntisocial personality disorderPsychologyMeta-analysisClinical psychologyPsychopathyPsychiatryConduct disorderPersonalityMedicineInjury preventionPoison controlSocial psychologyMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

The effectiveness of treatment for individuals diagnosed with Antisocial Personality Disorder (ASPD) has been questioned and debated for years. As individuals with ASPD are considerably overrepresented in the criminal justice system, the ability of treatment to reduce recidivism is a prominent concern. The present meta-analysis identified six unique controlled and uncontrolled treatment outcome studies investigating the effectiveness of treatment in reducing general/any recidivism for individuals with ASPD. Results from the controlled studies indicated no significant differences in recidivism rates between individuals with ASPD in treatment and those in treatment as usual; however, the direction of the odds ratios suggested lower recidivism for the treatment groups. Results from the uncontrolled studies suggested equal effectiveness of treatment when comparing individuals with and without ASPD; however, these effects may not be attributable to the treatment in question. Interpretation of these findings and the generalizability of the general offender treatment literature to individuals with ASPD is discussed.

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)0.004
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 score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.079
GPT teacher head0.388
Teacher spread0.310 · 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

Citations47
Published2014
Admission routes1
Has abstractyes

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