A psychometric examination of treatment change in a multisite sample of treated Canadian federal sexual offenders.
Bibliographic record
Abstract
In the present study, we examined the degree of change and predictive accuracy of a number of well-known psychological self-report measures intended to identify treatment targets for sexual offenders. Participants included 392 federally incarcerated sexual offenders who participated in low, moderate, or high intensity sexual offender programs offered within penitentiaries under the jurisdiction of the Correctional Service of Canada. These men were followed in the community for an average of 5.42 years postrelease. Very small to moderate pretreatment and posttreatment changes were found on measures of cognitive distortions, aggression/hostility, empathy, loneliness, social intimacy, and sex offender acceptance of responsibility. However, pretreatment and posttreatment scores on these measures frequently demonstrated weak and inconsistent relationships to sexual, violent, and general recidivism. In addition, within-treatment change on these measures bore little relationship to outcome. However, when statistically corrected for pretreatment score the relationship of treatment change to outcome frequently improved, particularly on measures of physical aggression and anger, even after controlling for Static-99R score. Clinical and research implications are discussed regarding the assessment and evaluation of change on psychological risk factors in treated sexual offenders.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".