Reasons for Using Roll-Your-Own Tobacco and Perceptions of Health-Promoting Pack Inserts: A Focus Group Study with Roll-Your-Own Tobacco Smokers in Scotland
Bibliographic record
Abstract
INTRODUCTION: Use of roll-your-own (RYO) tobacco is increasing in most regions, but few qualitative studies have explored why RYO smokers use this product, and no study has considered their views of health-promoting pack inserts. METHODS: Eight focus groups were conducted with 18-35-year-old RYO smokers (n = 50) in Greater Glasgow (Scotland) in February-March 2020 to explore reasons for using RYO and perceptions of health-promoting inserts. Participants were shown four inserts adapted from those required in cigarette packs in Canada, with all encouraging quitting, and two RYO-specific inserts explaining that RYO is not less harmful than cigarettes. RESULTS: Lower price, better taste, the pleasure of rolling and ability to customize roll-ups, and the belief that RYO was less harmful than cigarettes were drivers for use. There were mixed perceptions of the extent to which inserts would capture attention if included in RYO packs. The positive messaging used on the Canadian inserts was considered motivational and inspirational, and contrasted with the on-pack warnings. The messaging on the RYO inserts, in comparison, was viewed unfavorably and generally dismissed. Participants, most of whom were not interested in quitting, did not feel that inserts would lead them to change their smoking behavior. However, some felt that the Canadian inserts could be helpful for those thinking about quitting and young people contemplating smoking. CONCLUSIONS: Inserts with positive messaging about quitting, rather than messaging explicating the harms of RYO, were preferred by RYO smokers. What, if any, RYO-specific messaging resonates with RYO smokers merits further attention. IMPLICATIONS: Aside from price, taste, and the pleasure associated with rolling and ability to individualize roll-ups, the erroneous belief that RYO is less harmful than cigarettes was a key reason for use. While inserts with positive messaging about quitting, as used on the Canadian inserts, were viewed as potentially helpful, inserts that challenged the idea that RYO was not less harmful than cigarettes were generally dismissed. Research is needed to understand what types of RYO-specific messaging could most effectively be used on inserts, or indeed in other media, to challenge the misperceptions that many RYO smokers hold.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".