Cross-country content analysis of e-cigarette packaging: a codebook and study protocol
Bibliographic record
Abstract
Introduction. Marketing elements on packaging can influence the appeal of electronic cigarette (EC) products. ECs can contain nicotine and their long-term health effects are unknown; it is important to monitor elements such as packaging which may influence the appeal and uptake of EC products by youth. This study therefore aims to describe marketing elements used on the packaging of commonly used EC products in England, Canada, and the US, countries with different EC marketing regulations. Methods and analysis. We will conduct two content analyses of EC products and their packaging. The first will focus on liquid-containing products (disposable devices, e-liquid refills) in Canada, England, and the US; the second on EC devices (tank, cartridge, and disposable) in England. We will use a codebook to systematically record elements present on EC products and their packaging, including: warnings, product information (e.g., flavour and nicotine), characteristics and design of the packaging and the product (e.g., shape, size), claims (e.g., health-related, cost-related), digital and interactive elements, colours, other graphic elements, and coder impressions. EC products will be sampled based on the most popular brands identified from surveys conducted by the International Tobacco Control Policy Evaluation Project (ITC) and Action on Smoking and Health (ASH). Approximately 144 products will be sampled for the cross-country analysis and 45 for the single-country analysis. We will report frequencies of each element, discuss frequently identified codes, and describe any notable differences between countries and product types. Ethics and dissemination. No ethical concerns. Results will be submitted for publication in a peer-reviewed journal.
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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 | 0.059 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".