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Record W2900339481 · doi:10.1177/0093854818808830

Reducing Violence Risk? Some Positive Recidivism Outcomes for Canadian Treated High-Risk Offenders

2018· article· en· W2900339481 on OpenAlexaffabout
Tamsin Higgs, Franca Cortoni, Kevin L. Nunes

Bibliographic record

VenueCriminal Justice and Behavior · 2018
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton UniversityUniversité de Montréal
Fundersnot available
KeywordsRecidivismIndigenousPoison controlInjury preventionSuicide preventionHuman factors and ergonomicsPsychologyEthnic groupOccupational safety and healthDemographyClinical psychologyMedicineMedical emergencyPolitical scienceSociology

Abstract

fetched live from OpenAlex

In pursuit of “what works” in violent offending behavior programs, there remain insufficient evaluations of program outcomes. Three hundred forty-five offenders from the Canadian Violence Prevention Program (VPP) were compared after an average 3-year follow-up with 338 non-VPP participants. Outcomes measured were new convictions for violent, sexual, or general offenses. Intent-to-treat design was used. Subsequently, participants who completed or did not complete the program were compared with the non-VPP group. Further analyses considered Indigenous and non-Indigenous subgroups. Overall, lower recidivism rates were associated with VPP completion, both in the complete sample and ethnic subgroups. However, the main finding of significantly lower likelihood of violent recidivism was found only for the Indigenous offenders, while significantly lower likelihood of general (nonviolent) recidivism was specific to non-Indigenous offenders. Results are interpreted cautiously in relation to program effectiveness given the quasi-experimental design and the important implications of outcome studies for correctional services.

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 score0.707
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 studies0.0010.000
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 head0.043
GPT teacher head0.333
Teacher spread0.291 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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