Association of fully branded, standardized packaging and limited flavor and brand descriptors of e‐liquids with interest in trying products among youths in Great Britain
Bibliographic record
Abstract
BACKGROUND AND AIMS: Many vaping products feature bright colors and novel brand names and flavor descriptors, which may appeal to youth. We measured the strength of the associations between e-liquid packaging design (branded, white standardized or white standardized limiting brand and flavor descriptors) and perceived peer interest in trying the e-liquids among youth. DESIGN: A between-subjects online experiment. SETTING: The Action on Smoking and Health Smokefree Great Britain (GB) Youth 2021 online survey. PARTICIPANTS: Participants included 1628 youth aged 11-18, 51.9% female, 71.8% socioeconomic status ABC1 (the three highest Market Research Society grades). MEASUREMENTS: Participants were randomized to view a set of three images of e-liquids from one of three packaging conditions: (1) fully branded (control), (2) white standardized with usual brand names and flavor descriptors or (3) white standardized with coded brand names and limited flavor descriptors. Participants were asked which e-liquid they thought people their age would be most interested in trying and could select a product, 'none of these', or 'do not know'. Multinomial logistic regression models were used to test associations between selecting 'none of these' ('no interest') versus any product ('interest') or 'do not know' and packaging condition. Analyses were adjusted for sex, age, socioeconomic status, vaping status and smoking status. FINDINGS: Compared with fully branded packaging (22.7%; reference category), youth had higher odds of reporting no perceived peer interest in trying e-liquids in standardized packs with brand codes and limited flavor descriptors [30.3%, adjusted odds ratio (AOR) = 2.07, 95% confidence interval (CI) = 1.53-2.79], but not standardized packs with usual descriptors (23.1%, AOR = 1.21, 95% CI = 0.89-1.65). Youth had higher odds of reporting no perceived peer interest in e-liquids in white standardized packs with brand codes and limited flavor descriptors (30.3%, AOR = 1.87, 95% CI = 1.29-2.16, P < 0.001) compared with standardized packs with usual descriptors (23.1%; reference category). CONCLUSION: Standardized e-liquid packaging that limits flavor and brand descriptors may reduce the youth appeal of e-liquids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".