Professionals’ Understandings of and Attitudes to the Prevention of Sexual Abuse: An International Exploratory Study
Bibliographic record
Abstract
Sexual abuse is a global issue and, therefore, responding to and preventing sexual abuse are global challenges. Although we have examples of and evidence for sexual abuse prevention initiatives internationally, these tend to come from a small, select group of countries (i.e., United Kingdom, United States, Canada, Germany, Netherlands, New Zealand, and Australia) and not from a broader global pool. This article will present the qualitative data from an online study ( n = 82), covering 17 countries, on professionals’ (i.e., people working in the arena of sexual offending from a clinical, criminal justice, policy, research, and/or practice perspective) perceptions sexual abuse prevention in theory, practice, and policy. The article identifies three main themes: (a) professionals’ understandings of the prevention of sexual abuse, (b) public understanding of sexual abuse prevention, and (c) governmental attitudes towards, and support of, sexual abuse prevention programs. The article highlights that, although there are similar understandings of sexual abuse prevention internationally, practice is characterised by national differences in the funding of, provision of, and public/policy perceptions of prevention as well as its impact on offending.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".