Effect of Viewing Smoking Scenes in Motion Pictures on Subsequent Smoking Desire in Audiences in South Korea
Bibliographic record
Abstract
Background: In the modern era of heightened awareness of public health, smoking scenes in movies remain relatively free from public monitoring. The effect of smoking scenes in movies on the promotion of viewers’ smoking desire remains unknown. Objective: The study aimed to explore whether exposure of adolescent smokers to images of smoking in fılms could stimulate smoking behavior. Methods: Data were derived from a national Web-based sample survey of 748 Korean high-school students. Participants aged 16-18 years were randomly assigned to watch three short video clips with or without smoking scenes. After adjusting covariates using propensity score matching, paired sample t test and logistic regression analyses compared the difference in smoking desire before and after exposure of participants to smoking scenes. Results: For male adolescents, cigarette craving was significantly higher in those who watched movies with smoking scenes than in the control group who did not view smoking scenes (t307.96=2.066, P<.05). In the experimental group, too, cigarette cravings of adolescents after viewing smoking scenes were significantly higher than they were before watching smoking scenes (t161.00=2.867, P<.01). After adjusting for covariates, more impulsive adolescents, particularly males, had significantly higher cigarette cravings: adjusted odds ratio (aOR) 3.40 (95% CI 1.40-8.23). However, those who actively sought health information had considerably lower cigarette cravings than those who did not engage in information-seeking: aOR 0.08 (95% CI 0.01-0.88). Conclusions: Smoking scenes in motion pictures may increase male adolescent smoking desire. Establishing a standard that restricts the frequency of smoking scenes in films and assigning a smoking-related screening grade to films is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".