Exposure to Movie Smoking Among US Adolescents Aged 10 to 14 Years: A Population Estimate
Bibliographic record
Abstract
BACKGROUND: Several studies have linked seeing smoking in movies with adolescent smoking, but none have determined how much movie smoking adolescents see. OBJECTIVE: Our aim was to determine exposure to movie smoking in a representative sample of young US adolescents. METHODS. We surveyed 6522 nationally representative US adolescents aged 10-14 years. We content analyzed 534 contemporary box-office hits for movie smoking. Each movie was assigned to a random subsample of adolescents (mean: 613) who were asked whether they had seen the movie. Using survey weights, we estimated the total number of US adolescents who had seen each movie and then multiplied by the number of smoking depictions in each movie to obtain gross smoking impressions seen by adolescents. RESULTS: The 534 movies were mainly rated PG-13 (41%) and R (40%), and 74% contained smoking (3830 total smoking occurrences). On average, each movie was seen by 25% of the adolescents surveyed. Viewership was higher with increased age and lower for R-rated movies. Overall, these movies delivered 13.9 billion gross smoking impressions, an average of 665 to each US adolescent aged 10-14 years. Although this sample's R-rated movies contained 60% of smoking occurrences, they delivered only 39% of smoking impressions because of lower adolescent viewership. Thirty popular movies each delivered > or =100 million gross smoking impressions. Thirty actors each delivered >50 million smoking impressions, such that just 1.5% of actors delivered one quarter of all character smoking to the adolescent sample. CONCLUSIONS: Popular movies deliver billions of smoking images and character smoking depictions to young US adolescents. Removing smoking from youth-rated films would substantially reduce exposure from new box-office hits. Furthermore, the popular actors who frequently smoke in movies could have a major impact on adolescent movie smoking exposure by choosing not to portray characters who smoke.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".