The Role of Media on the Intention of Adolescents Smoking: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Aims: Smoking in adolescents show worrying trends, and at this age, media threats and media attention increases. Therefore, to determine the role of media in the intentions to smoke in adolescents, this study was conducted. Information & Methods: The systematically electronic search from international databases including Medline, Web of Science, Scopus, and Science Direct until April 2019 was conducted, and observational studies addressing the associations between media exposure and smoking in adolescents aged 10 to 19 years were analyzed. The results of related studies were evaluated with the Newcastle and Ottawa Guidelines and the JBI (The Joanna Briggs Institute), and Chi2 and I2 statistics assessed between-studies heterogeneity. Findings: In this study, 20 articles were entered, and 13 articles were included in the meta-analysis. The ORs (95% CI) exposure to the media contents associated with intentions to smoke in adolescents was as follows: in persuasive media OR=1.27 (95% CI 0.98, 1.64) and protective media OR=0.76 (95% CI 0.67, 0.86) with the intention of smoking in adolescents. Also, films with cigarette content were considered the strongest media were encouraging the intention to smoke OR=1.54 (95% CI 0.91, 2.59). Conclusion: The present study provided a clear picture of mediachr('39')s role and how a medium platform influences the intention of smoking among adolescents and emphasizes the need to acquire and improve media literacy skills to prevent them.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".