Testosterone, Sexual Offense Recidivism, and Treatment Effect Among Adult Male Sex Offenders
Bibliographic record
Abstract
The relationship between serum testosterone and sexual violence was examined in a sample of 501 convicted adult male sex offenders attending an intensive in-hospital group psychotherapy treatment program. It was found that men with higher testosterone tended to have committed the most invasive sexual crimes (p < .001, two-tailed). Further, a positive partial correlation (controlling for age) between testosterone and sexual offense recidivism over a lengthy follow-up period (mean = 8.9 years) was found. When the sample was separated into one group that completed treatment and one group that did not, an important ameliorating treatment effect was observed. Although controlling for age, serum testosterone remained significantly predictive of sexual recidivism for the treatment noncompleter group (p < .05, two-tailed). For those who completed treatment testosterone was no longer predictive of sexual reoffense (p > .05, two-tailed). Among convicted sex offenders, higher serum testosterone appears to be associated with greater likelihood of further sexual violence. Effective therapy, however, appears able to intercede in the influence of testosterone on sexually deviant behavior. It is suggested that serum testosterone may be an informative static risk factor and completion of intensive treatment should be accorded significance in future actuarially based risk prediction instruments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".