Predicting Tobacco Smoking among Adolescents Using Social Capital and Media Exposure with Theory of Planned Behavior:
Bibliographic record
Abstract
Tobacco smoking remains an ongoing and dire public health threat globally. Identifying factors that influence individuals’ smoking behavior is critical especially among adolescents. This study aimed to determine the effects of media exposure to tobacco advertisement, social capital, and other factors, on tobacco smoking among adolescents using Theory of Planned Behavior (TPB). This cross-sectional study was conducted in KulonProgo District, Yogyakarta Province, Indonesia, in April 2018. The dependent variable was smoking behavior. The independent variables were intention to smoke, attitude toward smoking, knowledge about tobacco use, subjective norm toward smoking, perceived behavior control not to smoke, media exposure to cigarette advertisement, and social capital among peer adolescents. The data were collected by questionnaire and analyzed by path analysis run on Stata 13. The TPB constructs including attitude toward smoking (b = 0.90; 95% CI = 0.29 to 1.51; p = 0.004), subjective norm toward smoking (b = 1.59; 95% CI = 0.99 to 2.19; p < 0.001), and perceived behavior control not to smoke (b = -2.07; 95% CI = -2.68 to -1.45; p < 0.001), had impact on intention to smoke and smoking behavior. Exposure to tobacco advertisement had indirect impact on smoking behavior through attitude toward smoking and intention to smoke. Weak social capital had indirect impact on smoking behavior through subjective norm toward smoking and intention to smoke. It concludes thatTPB can be used to explain smoking behavior among adolescents.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".