Protective Factors Related to Desistance in Sexual Offending: A Scoping Review
Bibliographic record
Abstract
Although most offenders who have committed a sex crime will not reoffend, an excessive amount of attention has been paid to the process that leads a minority to commit a new offense. What are the protective factors that contribute to the absence of recidivism among most of these sex offenders? This scoping review provides an overview of the current state of the literature on desistance among sex offenders as well as a list of the empirically tested protective factors that contribute to it. Peer-reviewed articles and grey literature were retrieved through database searches and reference harvesting following the elaboration of an internal grid composed of approximately 20 keywords and specific inclusion criteria. Articles were included if the majority of each study’s sample had committed a sex offense, factors explaining desistance from sexual offending were explicitly addressed, and all participants in the various studies were still considered desistors at the time of our search. From a database of 6,556 articles published between 1985 and 2022, 26 studies were retained, and more than 150 different protective factors were identified and grouped into 32 distinct subcategories. Our analysis revealed that the selected studies conceptualize desistance differently and that this choice not only affects the protective factors identified but could also influence ideas about how to intervene with sex 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".