A narrative review of research on clinical responses to the problem of sexual offenses in the last decade
Bibliographic record
Abstract
Research on the treatment and programs for people who have committed sexual offenses has greatly increased in the past decade. The aim of this review is to discuss research that has been published over that period (2010-2019) that is relevant for treatment providers. The articles included in this review were found through PsycINFO and PubMed (Medline) using the keywords "treatment or therapy" and "sex offen*". The inclusion criteria were publications that discuss treatment of persons who have committed sexual offenses (written in the English language only). Any articles that examined only special populations were excluded, such as those that examined persons who committed sexual offenses who were female, had intellectual disabilities, deafness, juveniles, etc., because these groups will likely have needs and responsivity factors that differ from the "average" natal-born male sex offender. Results showed that several meta-analyses indicate that treatment is effective in reducing sexual recidivism. The most frequently used treatment for sex offenders is cognitive behavioral therapy, which is often provided in conjunction with pharmacological treatment to reduce sexual impulsivity and/or sex drive. This review is limited to the specific key search terms. The findings of this review support the use of treatment and a community reintegration approach when treating persons who have committed sexual crimes to prevent sexual recidivism.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".