Time spent on smartphones are associated with exposure to online tobacco advertisements among youths
Bibliographic record
Abstract
Objective Smartphones are considered a double-edged sword in the tobacco control field. Ideally, the device can be used for efficiently delivering prevention and cessation programs. However, there are also concerns adolescents may be receiving pro-smoking messages via smartphones. This study aimed to examine the association between time spent on smartphones and exposure to online tobacco advertisements among youths. Methods The 13th (2017) Korean Youth Risk Behavior Survey, a nationally representative data of South Korean adolescents, were used. Along with the exposure to online tobacco advertisements during the past 30 days, smartphone use, and average time spent on smartphones per day were measured. Time spent were defined in quantiles. Multiple logistic regression analyses were performed, adjusting for sex, grade and perceived household wealth. Results Among 62,276 adolescents, 87.7% used smartphones during the past 30 days. The average time spent on smartphones per day was 266.8 minutes (4.4 hours). 39.6% of smartphone non-users were exposed to online tobacco advertisements, whereas 44.0% of users were exposed. More time spent on smartphones was associated with more exposure to online advertisements. Odds ratio estimates (reference=smartphone non-use) were as follows: Q1 (1.07, 95% CI=1.01-1.13), Q2 (1.20, 95% CI=1.13-1.27), Q3 (1.24, 95% CI=1.17-1.31) and Q4 (1.33, 95% CI=1.26-1.41). Conclusions Our results indicate a dose-response relationship between the time spent on smartphones and exposure to online tobacco advertisements. This implies tobacco advertisements are accessible through smartphones among youths. Tobacco advertisements on the internet is a violation to the article 13 of the WHO Framework Convention Tobacco Control. Further research and control measures are required to tackle online advertisements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".