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Record W2101494363 · doi:10.1177/0093854811403123

Evaluation of a Violence Risk (Threat) Assessment Training Program for Police and Other Criminal Justice Professionals

2011· article· en· W2101494363 on OpenAlexaff
Jennifer E. Storey, Andrea L. Gibas, Kim Reeves, Stephen D. Hart

Bibliographic record

VenueCriminal Justice and Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRisk assessmentVignetteCriminal justicePsychologyPoison controlSuicide preventionHuman factors and ergonomicsInjury preventionRecidivismApplied psychologyMedicinePsychiatryCriminologyMedical emergencyComputer securitySocial psychology

Abstract

fetched live from OpenAlex

Although a great deal of research has focused on the development and validation of violence risk (threat) assessment instruments, few studies have examined whether professionals can be trained to use these instruments. The present study evaluated the impact of a violence risk assessment training program on the knowledge, skills, and attitudes of 73 criminal justice professionals, including police officers, civilian support staff, and prosecutors. The program covered principles of violence risk assessment, the nature of mental disorder and its association with violence risk, and the use of various structured professional judgment (SPJ) risk assessment instruments. Comparisons of pre- and post-training evaluations indicated significant improvements on measures of knowledge about risk assessment, skills in the analysis of risk in a case vignette, and perceived confidence in conducting violence risk assessments. Findings support the utility of risk assessment training for criminal justice professionals and the utility of SPJ violence risk assessment instruments generally.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.244
GPT teacher head0.474
Teacher spread0.231 · 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 designOther design
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

Citations44
Published2011
Admission routes1
Has abstractyes

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