Peer-to-Peer Social Media as an Effective Prevention Strategy: Quasi-Experimental Evaluation
Bibliographic record
Abstract
BACKGROUND: Substance use by adolescents remains to be at unacceptably high levels, and there is evidence that teens' social norms are becoming more favorable toward recreational use and perceived safety of substances such as marijuana and prescription opioids. Social media offer a low-cost, potentially high-impact approach to disseminate prevention messages. OBJECTIVE: Living the Example (LTE) is a program that trains adolescent youth ambassadors to develop and disseminate prevention messages within their own social media networks and through in-school activities. This study aimed to evaluate the effects of exposure to LTE-based social media on students in the youth ambassadors' networks. METHODS: The George Washington (GW) University designed and implemented a quasi-experimental evaluation of the LTE program in 3 Maryland high schools. Before program launch, a sample of 826 students (wave 1) at the 3 schools, drawn from a census of freshmen enrolled in a class attended by all students at the grade level, completed a survey. A total of 584 students were surveyed at the wave 2 program midpoint and 542 at the wave 3 endpoint. The survey contained questions on drug use-related attitudes, beliefs, intentions, and behaviors, all based on validated measures. We evaluated the effects of LTE on the intended next 30-day drug use, and controlling for LTE self-reported exposure, age, and gender from waves 2 and 3 was appended into a single dataset. We first conducted ordinal logistic regressions for each drug use intention in wave 3 (ie, sell or distribute illegal drugs, smoke cigarettes, drink beer/wine/hard liquor when parents do not know about it, use marijuana, use lysergic acid diethylamide, cocaine, amphetamines or other illegal drugs, use heroin, use synthetic drugs, and use any prescription pills without a prescription) to examine the association between LTE exposure and drug use intentions. We included an interaction term for the study wave to examine intervention effects. RESULTS: We found a significant positive effect of LTE exposure on all 8 measured drug use intentions: sell/distribute illegal drugs; smoke cigarettes; drink beer, wine, or liquor when my parents do not know about it; use marijuana; use cocaine, amphetamines, or other illegal drug; use heroin; use synthetic drugs; use any prescription pills without a prescription (all P<.05; odds ratios ranging from 2.12 to 3.71). We also found that boys were more likely than girls to exhibit reduced drug use intentions. We also found reductions in 30-day intentions between the second and third survey waves for all 8 measured drug use variables. CONCLUSIONS: Overall, the results are consistent with and indicate a stronger LTE effect in this study compared with a previous pilot study. LTE appears to offer a protective effect, with exposure to program messages leading to reduced/improved drug use intentions.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".