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Record W4411737151 · doi:10.1177/10790632251350625

An Efficient Measure of Sexual Interest in Children: The Revised Screening Scale for Pedophilic Interests (SSPI-2)

2025· article· en· W4411737151 on OpenAlexaff
Melissa O’Donaghy, Kelly M. Babchishin, Grace Culp, Rachael Zarbl, Alexis G. Hinkson

Bibliographic record

VenueSexual Abuse · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyRecidivismPedophiliaConstruct validityClinical psychologyDevelopmental psychologyPoison controlInjury preventionPsychometricsMedicineMedical emergency

Abstract

fetched live from OpenAlex

This study examined the convergent, divergent, predictive, and incremental validity of the Revised Screening Scale for Pedophilic Interests (SSPI-2) in a sample of 264 men adjudicated for sexual offenses against at least one child under the age of 15. We found evidence of construct validity as the SSPI-2 had small to medium correlations with phallometric testing ( r = .31), recorded pedohebephilic diagnoses ( r = .52), and attitudes tolerant of sexual offending against children ( r = .23), in addition to small and non-significant correlations with the PCL-R ( r = −.07), VRAG-R ( r = −.09), BARR-2002R ( r = −.06), and conduct disorder ( r = −.07). As indicated by DeLong tests, the SSPI-2 was a better predictor of 5-year sexual ( Z = −2.44) and non-contact sexual recidivism ( Z = −2.46) than the SSPI. The SSPI-2 also added incremental predictive accuracy to risk tools such as the BARR-2002R, PCL-R, VRAG-R, and Static-99R. Overall, our findings suggest that the SSPI-2 is a valid measure of sexual interest in children and may be useful as a screening tool to help inform prioritization and management.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.341
Teacher spread0.298 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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