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Record W4404444562 · doi:10.1093/ntr/ntae270

Opposition to Banning Cigarette Filters and the Belief That Removing Filters Makes Cigarettes Much More Harmful Among Adults Who Smoke: Findings From the 2022 International Tobacco Control Four Country Smoking and Vaping Survey

2024· article· en· W4404444562 on OpenAlexafffundabout
Shannon Gravely, Thomas E. Novotny, K. Michael Cummings, Katherine East, Andrew Hyland, Pete Driezen, Janet Hoek, Kylie Morphett, David Sellars, Richard J. O’Connor, Anne C K Quah, Geoffrey T. Fong, Coral Gartner

Bibliographic record

VenueNicotine & Tobacco Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersNational Health and Medical Research CouncilNational Cancer InstituteMedical Research CouncilCanadian Institutes of Health Research
KeywordsOpposition (politics)Tobacco controlSmokeEnvironmental healthNicotineSmoking cessationMedicineDemographyPsychologyPublic healthPolitical scienceLawGeographySociologyPolitics

Abstract

fetched live from OpenAlex

INTRODUCTION: In line with historical tobacco industry marketing claims, many consumers perceive cigarettes with filters as less harmful than cigarettes without filters. However, scientific evidence indicates that cigarette filters do not reduce the risks associated with smoking. We examined opposition to banning the sale of cigarettes with filters, beliefs about whether removing filters makes cigarettes much more harmful, and whether this belief is associated with opposition to banning filters among adults who smoke cigarettes from four high-income countries. AIMS AND METHODS: Data are from 2980 adults who smoke cigarettes and participated in the 2022 ITC Smoking and Vaping Survey in Australia, Canada, England, and the United States. Weighted descriptives estimated opposition to a cigarette filter ban and the belief that removing filters makes cigarettes "much more," "a little more," "not more" harmful, or "don't know." Adjusted regression analyses examined the association between opposition to banning filters (vs. support/don't know) and the belief that removing filters would make cigarettes much more harmful (vs. otherwise). RESULTS: Across all countries, 69.3% opposed banning filters, 11.5% of respondents supported banning filters, and 19.1% did not know (main effect for country differences: p = .001). Country differences remained significant after adjusting for covariates (p = .047), with adults who smoke in Australia and the United States being significantly more likely to oppose a filter ban than those in England. Canada did not differ significantly from any of the countries. Nearly half (45.9%) believe that removing filters would make cigarettes much more harmful, 28.6% reported a little more harmful, 15.3% were unsure, and 10.2% reported not more harmful (country differences: p = .002). Country differences were no longer significant after adjustment (p = .18). Believing that removing filters makes cigarettes much more harmful was strongly associated with opposing a filter ban (78.5%) (vs. otherwise: 62.1%, p < .001). CONCLUSIONS: Across all four countries, three-quarters of adults who smoke erroneously believe that removing filters would make cigarettes more harmful, and believing that doing so would make cigarettes much more harmful was the strongest predictor of opposing a filter ban. IMPLICATIONS: More than 90% of manufactured cigarettes worldwide contain filters. Contrary to marketing claims by the tobacco industry, cigarette filters do not offer any health protection from cigarette smoke; however, three-quarters of adults who smoke erroneously believe that cigarettes with filters are much less harmful than cigarettes without filters. To protect public health and the environment, the World Health Organization has recommended that policy-makers consider banning cigarette filters as they are unnecessary single-use plastics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.057
GPT teacher head0.341
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes3
Has abstractyes

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