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Record W3087213529 · doi:10.1177/0093854820958744

Evaluation of the Implementation of a Risk-Need-Responsivity Service in Community Supervision in Sweden

2020· article· en· W3087213529 on OpenAlexaff
Louise C. Starfelt, Marcus Dynevall, Johan Wennerholm, Sarah Åhlén, Tanya Rugge, Guy Bourgon, Charlotte Robertsson

Bibliographic record

VenueCriminal Justice and Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsRecidivismContext (archaeology)CognitionPsychologyService (business)Applied psychologyPoison controlMedicineClinical psychologyPsychiatryMedical emergencyBusiness

Abstract

fetched live from OpenAlex

The effective use of the core treatment principles from the Risk-Need-Responsivity (RNR) model has the potential to reduce criminal recidivism significantly. A pilot trial of the RNR-based model Krimstics in the Swedish probation service showed increased RNR adherence but no effects on recidivism. The subsequent implementation of Krimstics involved the training and clinical support of more than 700 probation officers working with community supervision. In parallel, an implementation evaluation examining RNR adherence was undertaken, collecting and coding audio-recorded supervision sessions and case file data. Findings showed that Krimstics-trained probation officers ( N = 96) used cognitive behavioral therapy-based techniques in supervision sessions while demonstrating moderate-to-high levels of relationship building skills. However, adherence to the risk principle was lacking and key cognitive behavioral techniques showed poor quality. Although Krimstics has increased RNR adherence in a Swedish context, challenges with implementing theory into practice may obscure the assessment of the service’s effectiveness.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.998

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 head0.151
GPT teacher head0.430
Teacher spread0.279 · 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.

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

Citations10
Published2020
Admission routes1
Has abstractyes

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