Self-help seeking for people concerned about their thoughts and behaviors regarding children during the COVID-19 pandemic
Bibliographic record
Abstract
Concerns about the impacts of the COVID-19 pandemic on child sexual exploitation and abuse have been expressed by police and child protection organizations. However, there are limited data about how the pandemic may have caused changes in the help-seeking behavior of people who have concerns about their thoughts and behaviors regarding children. In this study, we examine helpline and webpage metrics from two organizations - Stop It Now! USA and Lucy Faithfull Foundation's Stop It Now! UK and Ireland - providing resources about child sexual exploitation and abuse prevention, including self-help pages for people concerned about their sexual thoughts or behaviors involving children. Pages for self-help seekers were compared to pages for parents/caregivers and general audiences. Based on descriptive data, there was mixed evidence of an increased demand for self-help, with an increase in helpline inquiries and an increase in views for some but not all self-help pages. Webpage trends for self-help pages were not matched by similar trends for parent/caregiver or general information pages, suggesting the increase in demand was specific to self-help seeking. However, ANOVAs of both US and UK and Ireland webpage data did not result in a significant interaction effect between category (self-help, parent, or general) and time. While there were significant increases in self-help related helpline calls in the USA across time, all three types of helpline calls increased in the UK and Ireland. The implications of these data for self-help resources and perpetration prevention are discussed.
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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 | 0.003 | 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 | 0.002 | 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".