Protecting You/Protecting Me: Effects of an Alcohol Prevention and Vehicle Safety Program on Elementary Students
Bibliographic record
Abstract
This paper describes an evaluation of Protecting You/Protecting Me (PY/PM), a classroom-based, alcohol-use prevention and vehicle safety program for elementary students in first through fifth grades developed by Mothers Against Drunk Driving. PY/PM lessons and activities focus on teaching children about (I) their brains (why their brain is important, how their brain continues to develop throughout childhood and adolescence, what alcohol does to the developing brain, and why it is important to protect their brain); (2) vehicle safety (what to do to protect themselves should they ever ride with an impaired driver); and (3) life skills (decision making, stress management, and media literacy). Fourth- and fifth-grade students from schools in the fourth year of PY/PM implementation were surveyed. Results indicated that, relative to comparison students from matched schools, PY/PM students increased their knowledge of alcohol's effect on development; gained decision-making, stress-management, and vehicle safety skills; and demonstrated changes in attitudes toward underage alcohol use and its harm. Further, students retained lessons learned in previous years and their scores improved with increased exposure to PY/PM. In addition, the findings demonstrate that it is possible to design and implement a program that can improve young children's knowledge regarding alcohol and their developing brains, teach them skills to protect themselves in dangerous situations, increase already high antialcohol attitudes, and change perceptions of alcohol's harmfulness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".