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Record W1501057957 · doi:10.1002/bsl.2159

Evaluation and Applications of the Clinically Significant Change Method with the Violence Risk Scale‐Sexual Offender Version: Implications for Risk‐Change Communication

2015· article· en· W1501057957 on OpenAlexaff
Mark E. Olver, Sarah M. Beggs Christofferson, Stephen C. P. Wong

Bibliographic record

VenueBehavioral Sciences & the Law · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismDemographyRisk assessmentPsychologySexual violencePoison controlSex offenseScale (ratio)Injury preventionClinical psychologyMedicineSexual abuseComputer securityMedical emergencyComputer scienceCriminology

Abstract

fetched live from OpenAlex

We examined the use of the clinically significant change (CSC) method with the Violence Risk Scale-Sexual Offender version (VRS-SO), and its implications for risk communication, in a combined sample of 945 treated sexual offenders from three international settings, followed up for a minimum 5 years post-release. The reliable change (RC) index was used to identify thresholds of clinically meaningful change and to create four CSC groups (already okay, recovered, improved, unchanged) based on VRS-SO dynamic scores and amount of change made. Outcome analyses demonstrated important CSC-group differences in 5-year rates of sexual and violent recidivism. However, when baseline risk was controlled via Cox regression survival analysis, the pattern and magnitude of CSC-group differences in sexual and violent recidivism changed to suggest that observed variation in recidivism base rates could be at least partly explained by pre-existing group differences in risk level. Implications for communication of risk-change information and applications to clinical practice are discussed.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.271
GPT teacher head0.463
Teacher spread0.192 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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