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Record W4386391398 · doi:10.1177/00938548231196572

Protective Factors in Forensic Practice: The Added Value of the SAPROF-Extended Version Pilot in Relation to Aggressive Incidents

2023· article· en· W4386391398 on OpenAlexaff
Leen Cappon, Armin Jentsch, Saskia Roggeman, Michiel de Vries Robbé

Bibliographic record

VenueCriminal Justice and Behavior · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPredictive validityPredictive valuePsychologyHuman factors and ergonomicsPoison controlOccupational safety and healthIncremental validityClinical psychologyInjury preventionSuicide preventionPsychiatryMedicineTest validityMedical emergencyPsychometrics

Abstract

fetched live from OpenAlex

Protective factors are now commonly included in comprehensive risk assessment. This study concerns an initial validation of the new SAPROF-Extended Version (SAPROF-EV) pilot, containing modifications and additions to the original SAPROF. For 139 forensic psychiatric inpatients, assessment results with the SAPROF-EV pilot and HCR-20 V3 were compared with aggressive incidents. Results show good predictive validity for the SAPROF-EV pilot for all outcomes and incremental predictive validity for both the (modified) original SAPROF and the full SAPROF-EV pilot over the HCR-20 V3 . For the outcome aggression toward others, the additional SAPROF-EV factors provide incremental predictive validity over the (modified) original SAPROF. In addition, the user feedback from clinicians highlights experienced additional value of the new factors for treatment guidance. Based on the findings from this study, the SAPROF-EV pilot will be adjusted further into an improved and enhanced version of the SAPROF.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.057
GPT teacher head0.373
Teacher spread0.316 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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