Development of measures assessing attitudes toward contraband tobacco among a web-based sample of smokers
Bibliographic record
Abstract
BACKGROUND: As regulation of tobacco products tightens, there are concerns that illicit markets may develop to supply restricted products. However, there are few validated measures to assess attitudes or purchase intentions toward contraband tobacco (CT). As such, it is important to investigate individual level characteristics that are associated with the purchase and use of contraband tobacco. METHODS: In May 2013, a pilot survey assessed attitudes, behaviors, and purchase intentions for contraband tobacco based on previous research regarding non-tobacco contraband. The survey was administered via Amazon Mechanical Turk, a crowdsourcing resource, among current smoking respondents in the United States and Canada. Structural equation modeling was used to evaluate the validity of the proposed model for understanding attitudes toward contraband tobacco. RESULTS: CT purchasers were more likely to report norms supportive of counterfeit products, more intentions toward purchasing counterfeit products, a lowered risk associated with these products, and to have more favorable attitudes toward CT than those who had not purchased CT. Attitudes toward CT mediated the relationship between subjective norms and prior purchase with behavior intentions. Perceived risk had a significant direct effect on intentions and an indirect effect through attitudes toward CT. The structural model fit the data well and accounted for over half (53%) of the variance in attitudes toward tobacco. CONCLUSIONS: Understanding the mechanisms associated with CT attitudes and purchase behaviors may provide insight for how to mitigate possible iatrogenic consequences of newly implemented regulations. The measures developed here elucidate some elements that influence attitudes and purchase intentions for CT and may inform policy efforts to curtail the development of illicit markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".