Effectiveness of a predator avoidance program for elementary-aged youth
Bibliographic record
Abstract
Introduction With thousands of children abducted and abused each year, efforts are needed to keep children safe from predators. Revved Up Kids (RUK) is an intervention that gives elementary-aged children the necessary tools to recognize and avoid dangerous people and situations. The purposes of this study were to describe the RUK intervention components and document its effectiveness. Methods This evaluation utilized a quasi-experimental design to determine the effectiveness of RUK. The single-session intervention was offered in two formats: one-hour (n = 119 youth) and three-hour (n = 28 youth) workshops. RUK workshop effectiveness was compared to a comparison group (n = 211 youth) that did not receive an intervention. Data were collected at baseline, immediate-post, and 1-month follow-up from second to fourth grade participants. A series of linear mixed models were fitted. Results Compared to the comparison group, participants in both RUK workshops showed significant improvements across the three time points. More specifically, participants in the one-hour and three-hour RUK workshops significantly increased their safety knowledge measured by the Recognize Score (p < 0.01), Avoid Score (p < 0.01), and Escape Score (p < 0.01), respectively. Discussion These effective single-session workshops can be easily introduced into schools and community-based settings to complement existing efforts to prevent child abduction and abuse.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".