Structural and Convergent Properties of Structured Assessment of Protective Factors (SAPROF) Ratings in a Treated Sexual Offending Sample
Bibliographic record
Abstract
The Structured Assessment of Protective Factors (SAPROF) is a measure of protective factors intended to augment violence risk assessment. While prior research supports the predictive validity of SAPROF ratings, factorial and convergent validity have been underexamined, each of which is required to ensure that the instrument measures intended targeted constructs and converges with test scores from established measures. We evaluated the structural and convergent properties of SAPROF ratings through examining its factor structure and convergence with measures of relevant constructs, as a function of ethnocultural heritage, in a treated sample of 461 men with sexual offense conviction histories. The SAPROF was rated from institutional files pre-and posttreatment. Results of exploratory structural equation modeling (ESEM) of pre and post SAPROF item ratings identified a temporally stable three-factor model that was invariant across ethnocultural groups; however, it departed from the developers' original subscale structure-the factors were termed Internal-Prosocial, Motivational-Lifestyle, and External-to reflect continuity with, yet departure from, the current subscale structure. SAPROF ratings were correlated in theoretically and clinically meaningful ways with scores on relevant risk-need-responsivity (RNR) measures. The results support the structural and convergent validity of SAPROF ratings and identified a slightly modified subscale structure in the present sample.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".