The Size and Sign of Treatment Effects in Sex Offender Therapy
Bibliographic record
Abstract
We review scientific criteria for the minimally useful evaluation of psychosocial treatment for sex offenders. The Association for the Treatment of Sexual Abusers recently supported a meta-analysis ((Hanson et al., 2002)) of the effectiveness of psychological treatment for sex offenders. It was concluded that current treatments for sex offenders reduce recidivism. In this chapter, we reevaluate the evidence. Whereas the random assignment studies yielded results that provided no evidence of treatment effectiveness, Hanson et al. reviewed approximately a dozen others (called "incidental assignment" studies), which yielded substantial positive results for treatment. Upon close inspection, we conclude that such designs involve noncomparable groups and are too weak to be used to draw inferences about treatment effectiveness. In almost every case, the evidence was contaminated by the fact that comparison groups included higher-risk offenders who would have refused or quit treatment had it been offered to them. We conclude that the effectiveness of psychological treatment for sex offenders remains to be demonstrated. Furthermore, we outline solutions that we think will lead to progress in the field of sex offender treatment.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".