Child sexual exploitation material offenders, one‐size‐fits‐all for? Exploring tailored clinical dimensions based on cognitive and behavioural criminogenic factors
Bibliographic record
Abstract
BACKGROUND: Interventions for individuals involved in sexual offending behaviours are likely to be more effective if adapted to focus on their specific characteristics, suggesting that men who engage in sexual offences against children over the Internet should be treated differently from those who have actual physical contact against children. AIMS: The goal of this study is to explore possible associations between the criminogenic cognitions and behaviours of men using online child sexual exploitation material (CSEM) and variables relevant to intervention. We hypothesised that antisocial tendencies, sexual, emotional and relational problems and self-regulation problems, as well as cognitive distortions would be associated with CSEM use. METHOD: Ninety-eight men who had been convicted of at least one online CSEM-related offence, but no child contact sexual offences, at any time between 2001 and 2020 were recruited in the province of Quebec (Canada). Cases were reviewed to identify cognitive and behavioural criminogenic factors according to a coding sheet developed after reference to prior literature. Variables were extracted from official criminal records, sexological and psychological reports, as well as investigative and forensic reports and interviews. Exploratory factor analysis was carried out to identify potentially relevant dimensions. RESULTS: Analysis confirmed five distinct factors which, together, accounted for 60% of the variance: dissocial traits, dysfunctional intimacy, passive alienation (internalised sense of alienation), normless alienation (social norms experienced as alien) and coping with threat. CONCLUSION: Because online CSEM-related offenders present heterogeneous risks and needs, a 'one-size-fits-all' intervention is unlikely to be optimal for most of them. Our findings suggest a way of classifying risks and needs to facilitate more focused interventions. Future research should evaluate the relative effectiveness of general interventions compared to those tailored towards the dimensions of risks and needs identified in this study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.000 | 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".