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Violence Reduction Treatment of Psychopathy

2017· other· en· W2941822946 on OpenAlexaff
Stephen C. P. Wong, Keira C. Stockdale, Mark E. Olver

Bibliographic record

VenueThe Wiley Handbook of Violence and Aggression · 2017
Typeother
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychopathyPsychologyPsychopathy ChecklistBig Five personality traitsAntisocial personality disorderClinical psychologyDark triadOperationalizationPersonalityPoison controlDevelopmental psychologyInjury preventionSocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Abstract The treatment of psychopathy has a challenging history; psychopathy was even once deemed untreatable. Psychopathy, a personality disorder, is characterized by dysfunctional affective and interpersonal traits that frequently co‐occur with violence and criminal behaviors. The former traits are often operationalized by the Psychopathy Checklist–Revised Factor 1 (PCL–R Factor 1) and the latter behaviors by the PCL–R Factor 2. Research suggests treatment of psychopathy to reduce future violence and criminality is best directed toward reducing Factor 2 criminological features rather than changing Factor 1 personality traits. Treatment attrition and disruptions often associated with Factor 1 traits must be carefully managed to ensure treatment integrity. A two‐component model, based on the two‐factor conceptualization of psychopathy, has been proposed as a framework to guide violence reduction treatment of psychopathy. We review the psychopathy treatment literature, contrast major treatment approaches, and discuss the two‐component model with applications to different psychopathy subgroups.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

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