Tobacco point-of-sale advertising in Guatemala City, Guatemala and Buenos Aires, Argentina
Bibliographic record
Abstract
OBJECTIVES: To determine tobacco point of sale advertising prevalence in Guatemala City, Guatemala and Buenos Aires, Argentina. METHODS: Convenience stores (120 per city) were chosen from randomly selected blocks in low, middle and high socioeconomic neighbourhoods. To assess tobacco point of sale advertising we used a checklist developed in Canada that was translated into Spanish and validated in both countries studied. Analysis was conducted by neighbourhood and store type. RESULTS: All stores sold cigarettes and most had tobacco products in close proximity to confectionery. In Guatemala, 60% of stores had cigarette ads. High and middle socioeconomic status neighbourhood stores had more indoor cigarette ads, but these differences were determined by store type: gas stations and supermarkets were more prevalent in high socioeconomic status neighbourhoods and had more indoor cigarette ads. In poorer areas, however, more ads could be seen from outside the stores, more stores were located within 100 metres of schools and fewer stores had 'No smoking' or 'No sales to minors' signs. In Argentina, 80% of stores had cigarette ads and few differences were observed by neighbourhood socioeconomic status. Compared to Guatemala, 'No sales to minors' signs were more prevalent in Argentina. CONCLUSIONS: Tobacco point of sale advertising is highly prevalent in these two cities of Guatemala and Argentina. An advertising ban should also include this type of advertising.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".