Developing Smokeless Tobacco Prevention Messaging for At-Risk Youth: Early Lessons from “The Real Cost” Smokeless Campaign
Bibliographic record
Abstract
Introduction: Smokeless tobacco (SLT) use continues to be a significant public health challenge in the United States, particularly among young males in rural areas, where use remains disproportionately high. In support of the U.S. Food and Drug Administration's first nationwide SLT public education campaign, formative research was conducted to inform campaign strategy development and test creative concepts. Methods: Qualitative research methods were used to inform the strategic direction of the campaign, identify salient message themes, and refine creative concepts. Focus groups were conducted with 252 rural male youth ages 12–17 in seven states. Groups were organized by SLT status (i.e., at-risk for initiating vs. experimenting with SLT) and age group. Results: SLT use is culturally ingrained in rural communities, and rural youth are commonly exposed to SLT through close relationships. Among this group, “dipping” (SLT use) has strong cultural significance and is perceived as safe. Members of the target audience are receptive to straightforward facts delivered by authentic messengers about the potentially harmful consequences of SLT use, specifically those that leverage the progression of short-term consequences (e.g., white patches) to long-term health effects. Conclusions: This study addresses SLT literature gaps related to youth knowledge, attitudes, and beliefs by summarizing audience learnings from formative research that was used to develop the first national SLT public education campaign.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".