Awareness of and receptivity to FDA’s point-of-sale tobacco public education campaign
Bibliographic record
Abstract
The purpose of the study was to assess awareness of and receptivity to FDA's point-of-sale (POS) tobacco public education campaign for adult cigarette smokers called Every Try Counts; it was the first multi-county POS campaign in the U.S. The design was a county-level treatment-control three-wave longitudinal design. The setting was 15 treatment and 15 control counties. Subjects were smokers ages 25 to 54 (N = 3,628). 4,145 individuals screened in as eligible; 3,628 (87.5% response rate) completed the Wave 1 questionnaire (Wave 2: n = 2,812; Wave 3: n = 2,571; retention 70.9%). Measures were self-reported brand and ad awareness (saw any ad a few times or more) and receptivity (5-item perceived effectiveness scale). The analysis included descriptive analyses of receptivity; bivariate analyses of awareness by treatment group; and covariate- and time-adjusted logistic regression models to determine changes in awareness attributable to the campaign. Receptivity was moderate and differed significantly by race/ethnicity. As was the case for all waves, at wave 3, ad awareness was significantly higher in treatment (53.3%) than control counties (36.1%, p < .05). In regression models, brand (OR = 1.53, 95% CI: 1.26-1.86) and ad (OR = 1.74, 95% CI: 1.39-2.16) awareness were significantly higher in treatment than control counties. Every Try Counts generated a moderate level of receptivity and attention from cigarette smokers. Limitations include self-reports of campaign awareness and generalizability to a small number of U.S. counties.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".