The effectiveness of anti-illicit-drug public-service announcements: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Anti-illicit-drug public-service announcements (PSAs) have become a cornerstone of drug policy in the USA. However, studies of the effectiveness of these interventions have not been subjected to a systematic evaluation. METHODS: The authors searched 10 electronic databases along with major conference abstract databases (from inception until 15 February 2010) for all articles and abstracts that evaluated the effectiveness of anti-illicit-drug PSAs. The authors evaluated all studies that assessed intention to use illicit drugs and/or levels of illicit-drug use after exposure to PSAs, and conducted meta-analyses of these studies. RESULTS: The authors identified seven randomised trials (n=5428) and four observational trials (n=17 404). Only one randomised trial showed a statistically significant benefit of PSAs on intention to use illicit drugs, and two found evidence that PSAs significantly increased intention to use drugs. A meta-analysis of eligible randomised trials demonstrated no significant effect. Observational studies showed evidence of both harmful and beneficial effects. CONCLUSION: Existing evidence suggests that the dissemination of anti-illicit-drug PSAs may have a limited impact on the intention to use illicit drugs or the patterns of illicit-drug use among target populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".