A Systematic Review on the Effectiveness of Interventions for Individuals Who Have Committed Online Sexual Crimes Involving Children
Bibliographic record
Abstract
Objective: Online sexual crimes involving children have increased significantly with greater public access to the internet, underscoring the pressing need for effective intervention programs. While previous reviews attest to the effectiveness of psychological interventions for people who commit sexual offenses, none have specifically addressed those who commit sex crimes involving children using the internet. This systematic review aims to evaluate the effectiveness of psychological intervention programs targeting these crimes, identifying the most effective approaches in behavioral change. Method: The research was conducted across five databases (Scopus, B-on, APA PsycNet, PubMed, and Sage Journals), including studies evaluating the effectiveness of any psychological intervention in adults who have committed sexual crimes involving children through the internet, resulting in nine relevant studies. Most studies employed pre-post designs assessing psychological factors linked to reoffending, emphasizing integrated approaches including cognitive-behavioral therapy, life satisfaction promotion, multimodal treatments, and monitoring. Results: Findings indicated that, in most cases, there were psychological improvements related to criminal behavior, along with a reduction in the frequency and severity of offenses. Methodological limitations were common, such as reliance on pre-post designs, self-reported data, and absence of control groups or conditions. Conclusions: This review underscores the necessity for further research to gauge intervention program effectiveness in this population, stressing larger sample sizes, control group inclusion, and follow-up periods. Despite limitations, the review offers practical insights for clinicians, identifying key treatment components and risk assessment tools useful for personalized intervention planning and clinical decision-making in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".