Factors Affecting E-Smoking Behavior in Public Health Students of University Muhammadiyah Kalimantan Timur
Bibliographic record
Abstract
Background The trend of using electronic cigarettes (vapor) has developed rapidly among teenagers in the United States, and the largest increase occurred in the United States and Canada in 2018. The increased use of electronic cigarettes occurred from 2011 to 2015 while the decreased use occurred in 2016 and 2017. However, in 2018, the National Youth Tobacco Survey identified the increased use of vapor among adolescents; a 30-day trial of e-cigarette use increased by 20.8% among adolescents, especially students. The results of a survey conducted 2.5% using e-cigarettes. Objectives This study aims to investigate factors that influence the electric smoking behavior of public health students. Methods This study employed quantitative research with a cross-sectional approach to determine the relationship of behavioral factors to e-smoking. This study involved 214 samples selected using simple random sampling. Results Factors that influence smoking behavior are knowledge with a p-value of 0.000 and attitude with a p-value of 0.000. Moreover, this study has found that modern women use e-cigarettes because they consider that it does not violate any rule. Conclusions Factors that influence smoking behavior are knowledge, attitude. Influential factors are knowledge and attitudes so it is necessary to prevent the use of e-cigarettes with health education, regulations
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".