Abstract 18094: Appalachian Youth and Tobacco Products: Perceptions, Attitudes, and Behaviors
Bibliographic record
Abstract
Crucial to ending the scourge of tobacco addiction is reducing youth use of the various products. Despite reduction in youth cigarette use, other products (e.g., hookah, e-cigs) have increased in popularity in this age group (CDC, 2013, 2015). Use patterns are also influenced by factors such as social acceptance, peer use, SES, and accessibility (US DHHS, 2012, 2015). Some areas have higher youth tobacco use rates (Kruger et al., 2012; US DHHS, 2012, 2015); for example, use of tobacco by middle and high school students in Appalachia continues to exceed rates in other areas. Increased understanding of the factors promoting this use is vital to developing effective youth anti-tobacco campaigns and public policy. Objective: To assess Appalachian middle and high school students’ perceptions of, attitudes about, and use of tobacco products. Methods: Data were collected in three Appalachian states: Kentucky, New York, and North Carolina. These states were selected for differences in overall use of tobacco by youth (i.e., Kentucky: high, North Carolina: mid-level, New York: low). From October 2014-May 2015, 23 focus groups were conducted with middle and high school students (N=203). Each participant completed a questionnaire on perceptions of and attitudes about tobacco, use of tobacco, and views of health consequences and engaged in an open-ended focus group discussion. Results: Traditional tobacco products continue to be popular with Appalachian youth. For example, 29% of the participants have tried at least one tobacco product, with significant numbers of daily users (9% smokeless tobacco; 6% cigarettes). E-cig use was nearly as high, with approximately 22% reporting some experience. Findings also revealed varying attitudes and discrepant conclusions surrounding e-cigs. Comparisons between tobacco users and nonusers as well as by middle and high school status and state will be addressed.
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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 | 0.002 | 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) | 0.014 | 0.002 |
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".