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Record W2921783002 · doi:10.1177/0093854819834718

Forensic Assessment With the PAI in Correctional Samples: Implications for RNR

2019· article· en· W2921783002 on OpenAlexafffund
Carissa Toop, Mark E. Olver, Sandy Jung

Bibliographic record

VenueCriminal Justice and Behavior · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMacEwan UniversityUniversity of Saskatchewan
FundersMacEwan University
KeywordsRecidivismPsychologyMental illnessClinical psychologyPoison controlPersonalityPsychiatryHuman factors and ergonomicsInjury preventionDevelopmental psychologyMental healthSocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

The present investigation examined Risk-Need-Responsivity (RNR) correlates of Personality Assessment Inventory (PAI) scores in a sample ( N = 377) of offenders with diverse criminal histories. It was hypothesized that PAI scales with content reflective of criminogenic needs would be associated with recidivism while those indicative of major mental illness and behavioral disruption would be positively linked to responsivity variables. Several PAI scales predicted general and violent recidivism, particularly those reflective of criminogenic need. More serious profile patterns were associated with younger age, less education, lower cognitive ability, and sexual offense treatment attrition, per the responsivity principle. Finally, an exploratory factor analysis identified four PAI factors: Major Mental Illness, Extraversion, Paranoia, and Antisociality. Antisociality scores were the most predictive of general and violent recidivism. Antisociality and Major Mental Illness scores also predicted treatment attrition. Study findings suggest that the PAI can be a useful adjunct to standardized risk and need measures for RNR-informed assessment.

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 score0.408
Threshold uncertainty score0.530

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 studies0.0000.000
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 head0.064
GPT teacher head0.377
Teacher spread0.313 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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