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Extending health messaging to the consumption experience: a focus group study exploring smokers' perceptions of health warnings on cigarettes.

Crawford MoodieRachel O'DonnellJoy FlemingRichard PurvesJennifer McKellFiona Dobbie
Published in: Addiction research & theory (2019)
Introduction: While most countries require health warnings on cigarette packs, the Scottish and Canadian Governments are considering requiring health warnings on cigarette sticks. Methods: Twenty focus groups were conducted in Glasgow and Edinburgh (Scotland) with smokers (n = 120) segmented by age (16-17, 18-24, 25-35, 36-50, >50), gender and social grade, to explore perceptions of cigarettes displaying the warning 'Smoking kills' on the cigarette paper and any demographic differences in how smokers responded to these. Results: A warning on each cigarette was thought to prolong the health message, as it would be visible when a cigarette was taken from a pack, lit, left in an ashtray, and with each draw, and make avoidant behavior more difficult. That it would be visible to others was perceived as off-putting for some. It was felt that a warning on each cigarette would create a negative image and be embarrassing. Within several female groups they were viewed as depressing, worrying and frightening, with it suggested that people would not feel good smoking cigarettes displaying a warning. Within every group there was mention of warnings on cigarettes potentially having an impact on themselves, others or both. Some, mostly younger groups, mentioned stubbing cigarettes out early, reducing consumption or quitting. The consensus was that they would be off-putting for young people, nonsmokers and those starting to smoke. Conclusions: Including a warning on each cigarette stick is a viable policy option and one which would, for the first time, extend health messaging to the consumption experience.
Keyphrases
  • smoking cessation
  • healthcare
  • mental health
  • public health
  • replacement therapy
  • health information
  • physical activity
  • deep learning
  • machine learning
  • human health
  • climate change