An Evidence-Based Education Program for Adults about Child Sexual Abuse (“Prevent It!”) That Significantly Improves Attitudes, Knowledge, and Behavior
Bibliographic record
Abstract
Here we describe the development of an evidence-based education program for adults about childhood sexual abuse (CSA), called Prevent It! Uniquely, the primary goal of this program was to change the behavior of participants, as well as to increase knowledge about CSA and positive attitudes toward it. A comprehensive review shows no previous similar approach. The program includes a detailed manual to allow standardized administration by trained facilitators, as well as multiple video segments from CSA survivors and professionals. A total of 23 program workshops were run, with 366 adults participating. Of these, 312 (85%) agreed to take part in the study. All completed baseline ratings prior to the program and 195 (63% of study sample) completed follow-up assessments at 3-months. There were no significant differences between the demographic make-up of the baseline group and the follow-up group. Assessments included demographic data, knowledge, attitudes, and several measures of behavior (our primary outcome variable). Behavioral questions asked individuals to select behaviors used in the previous 3-months from a list of options. Questions also included asking "how many times in the previous 3-months" have you "talked about healthy sexual development or Child sexual abuse (CSA) with a child you know"; "suspected a child was sexually abused"; "taken steps to protect a child"; or "reported suspected sexual abuse to police or child welfare"? The majority of attendees were women, with the commonest age group being between 30 and 39 years old. Approximately 33% had experienced CSA themselves. At 3-month follow-up there were highly statistically significant improvements in several aspects of behavior and knowledge, and attitudes regarding CSA. For example, the number of subjects actively looking for evidence of CSA increased from 46% at baseline to 81% at follow-up, while the number of subjects who actively took steps to protect children increased from 25% at baseline to 48% at follow-up. For general public adults, this is the first program designed using the current evidence base for effective training in CSA examining longer-term outcomes and the first to focus on actual behavioral outcomes. The results suggest it is highly effective and support its widespread use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".