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Record W2167888287 · doi:10.1177/0093854809360043

Decision Making and Aggression in Forensic Psychiatric Inpatients

2010· article· en· W2167888287 on OpenAlexaff
Stephanie Bass, David Nußbaum

Bibliographic record

VenueCriminal Justice and Behavior · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOntario Shores Centre for Mental Health SciencesUniversity of Toronto
Fundersnot available
KeywordsTypologyAggressionPsychologyPsychological interventionClinical psychologyCognitionPoison controlIowa gambling taskHuman factors and ergonomicsEmpirical researchPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

This study provides initial empirical support for a novel neurobiological decision-making model proposed by Nussbaum (2005), applied to an aggression typology (Nussbaum, Saint-Cyr, & Bell, 1997). The Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) was analyzed for forensic inpatients using both the traditional method of scoring reflecting motivational decision making and a novel method developed by Yechiam, Busemeyer, Stout, and Bechara (2005) that provides scores for three cognitive decision-making components: attention, learning, and response-choice consistency. Predatory seclusions were predicted by traditional motivational scoring of the IGT but not by the cognitive scores. Conversely, Irritable seclusions were predicted only by the cognitive scoring system. Based on these findings, the utilization of the aggression typology and the inclusion of these clinical measures could enhance and refine violent risk assessment, suggest specific treatment targets for different aggression types, and monitor responses to interventions prior to release into the community.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.361
Teacher spread0.331 · 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 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

Citations24
Published2010
Admission routes1
Has abstractyes

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