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Record W2074907284 · doi:10.1007/s10979-008-9163-7

Deficits in behavioral inhibition predict treatment engagement in prison inmates.

2009· article· en· W2074907284 on OpenAlexaff
Diana Fishbein, Monica Sheppard, Christopher Hyde, Robert Hubal, David B. Newlin, Ralph C. Serin, George P. Chrousos, Salvatore Alesci

Bibliographic record

VenueLaw and Human Behavior · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsCarleton University
FundersNational Institute of Justice
KeywordsPsychologyNeurocognitiveClinical psychologyLegal psychologyPrisonCognitionDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Many inmates do not respond favorably to standard treatments routinely offered in prison. Executive cognitive functioning and emotional regulation may play a key role in treatment responsivity. During intake into treatment, inmates (N = 224) were evaluated for executive functioning, emotional perception, stress reactivity (salivary cortisol), IQ, psychological and behavioral traits, prior drug use, child and family background, and criminal histories and institutional behavior. Outcome measures included program completion, treatment readiness, responsivity and gain, and the Novaco Reaction to Provocation Questionnaire. Relative deficits in behavioral inhibition significantly predicted treatment outcomes, more so than background, psychological, or behavioral variables, and other neurocognitive and emotional regulatory measures. Future replications of these results have potential to improve assessment and treatment of offenders who are otherwise intractable.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.330
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2009
Admission routes1
Has abstractyes

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