Characteristics of the Users of Troubled Desire, a Web-Based Self-management App for Individuals With Sexual Interest in Children: Descriptive Analysis of Self-assessment Data
Bibliographic record
Abstract
BACKGROUND: Despite the high prevalence of child sexual offenses and the increasing amounts of available child sexual abuse material, there is a global shortage of preventive interventions focusing on individuals at risk of sexual offending. The web-based app Troubled Desire aims to address this shortage by offering self-assessments and self-management training modules in different languages to individuals with sexual interests in prepubescent and early pubescent children (ie, those with pedophilic and hebephiliac sexual interest, respectively). OBJECTIVE: The aim of this study was to describe the characteristics of the users of the Troubled Desire app. METHODS: The fully completed self-assessment data gathered within the first 30 months of this study from October 25, 2017 to April 25, 2020 were investigated. The main outcome measures were (1) sociodemographic information and (2) sexual interests and sexual behaviors of the users of Troubled Desire. RESULTS: The self-assessment was completed by 4161 users. User accesses were mainly from Germany (2277/4161, 54.7%) and the United States (474/4161, 11.4%). Approximately 78.9% (3281/4161) of the users reported sexual interest in children; these users were significantly more likely to report distress and trouble owing to their sexual interest. Further, child sexual offenses and consumption of child sexual abuse material were significantly more common among users with sexual interest in children than among users with no sexual interest in children. Additionally, the majority of the offenses were not known to legal authorities. CONCLUSIONS: The Troubled Desire app is useful in reaching out to individuals with sexual interest in prepubescent and early pubescent children. However, future research is warranted to understand the prospective relevance of the Troubled Desire app in the prevention of child sexual offending.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".