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Record W4363651508 · doi:10.1177/10731911231163617

Promotive, Mixed, and Risk Effects of Individual Items Comprising the SAPROF Assessment Tool With Justice-Involved Youth

2023· article· en· W4363651508 on OpenAlexafffund
Calvin M. Langton, Mackenzie Betteridge, James R. Worling

Bibliographic record

VenueAssessment · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPredictive validityOddsEconomic JusticeRisk assessmentClinical psychologySample (material)Human factors and ergonomicsSocial psychologyApplied psychologyPoison controlLogistic regressionEnvironmental healthMedicineComputer security

Abstract

fetched live from OpenAlex

The Structured Assessment of PROtective Factors for violence risk (SAPROF) is a widely used structured professional judgment (SPJ) tool. Its indices have predictive validity regarding desistance from future violence in adult correctional/forensic psychiatric populations. Although not intended for applied use with youth, SAPROF items lend themselves to an investigation of whether their operationalizations capture only strengths or also risks. With 229 justice-involved male adolescents followed for a fixed 3-year period, promotive, risk, and mixed effects were found. Most SAPROF items exerted a mixed effect, being associated with higher and lower likelihoods of violent and any reoffending at opposite ends of their trichotomous ratings. Summing items weighted using their promotive and risk odds ratios produced statistically significant improvements in predictive accuracy, improvements found also with a cross-validation sample of 171 justice-involved youth. The nature of strengths and implications for the development of SPJ tools and training in their use were 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.335
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes2
Has abstractyes

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