Tobacco messages encountered in real-time among low socio-economic position groups: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Tobacco advertising disproportionately targets low socio-economic position (SEP) groups, causing higher rates of tobacco use in this population. Anti-tobacco public health education campaigns persuade against use. This study measured real-time exposure of pro- and anti-tobacco messages from low SEP groups in two American cities. METHODS: Individuals in low SEP groups (N = 95), aged 18-34 years old, who were smokers and non-smokers, from the Boston and Houston areas, took part in a mobile health study. They submitted images of tobacco-related messages they encountered via a mobile application for a 7-week period. Two coders analyzed the images for message characteristics. Intercoder reliability was established using Krippendorff's alpha and data were analyzed descriptively. RESULTS: Of the submitted images (N = 131), 83 were pro-tobacco and 53 were anti-tobacco. Of the pro-tobacco messages, the majority were cigarette ads (80.7%) seen outside (36.1%) or inside (30.1%) a convenience store or gas station and used conventional themes (e.g., price promotion; 53.2%). Of the anti-tobacco messages, 56.6% were sponsored by public health campaigns or were signage prohibiting smoking in a public area (39.6%). Most focused on the health harms of smoking (28.3%). CONCLUSION: Low SEP groups in this study encountered more pro-tobacco than anti-tobacco messages at places that were point-of-sale using price promotions to appeal to this group. Anti-tobacco messages at point-of-sale and/or advertising regulations may help combat tobacco use.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".