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Record W4402843102 · doi:10.2196/63193

Association Between Cigarette and Bidi Purchase Behavior (Loose vs Pack) and Health Warning Label Exposure: Findings From the Tobacco Control Policy India Survey and In-Depth Interviews With People Who Smoke

2024· article· en· W4402843102 on OpenAlexaffvenue
Mayank Sakhuja, Daniela B. Friedman, Mark M Macauda, James R. Hébert, Mangesh S. Pednekar, Prakash C. Gupta, Geoffrey T. Fong, James F. Thrasher

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of WaterlooOntario Institute for Cancer Research
FundersNational Center for Chronic Disease Prevention and Health Promotion
KeywordsTobacco controlEnvironmental healthPackaging and labelingAdvertisingTobacco industryPsychologySmokePublic healthMedicineBusinessGeographyMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: The sale of loose cigarettes or bidis can undermine the purpose of requiring health warning labels (HWLs) on cigarette packs and bidi bundles by diminishing their visibility and legibility. OBJECTIVE: This mixed-methods study aims to examine the association between purchase behavior (loose vs pack or bundle), HWL exposure, and responses to HWLs among Indian adults who smoke. METHODS: Data were analyzed from the 2018-2019 India Tobacco Control Policy Survey and from 28 in-depth interviews conducted with Indian adults who smoked in 2022. The Tobacco Control Policy Survey sample included tobacco users who bought cigarettes (n=643) or bidis (n=730), either loose or in packs or bundles at their last purchase. Ordinal regression models were fit separately for cigarettes and bidis, whereby HWL variables (noticing HWLs, reading and looking closely at HWLs, forgoing a cigarette or bidi because of HWLs, thinking about health risks of smoking, and thinking about quitting smoking cigarettes or bidis because of HWLs) were regressed on last purchase (loose vs packs or bundles). In-depth interviews with participants from Delhi and Mumbai who purchased loose cigarettes in the last month were conducted, and thematic analysis was used to analyze the interview data. RESULTS: Survey findings indicated that about 74.3% (478/643) of cigarette users and 11.8% (86/730) of bidi users reported having bought loose sticks at their last purchase. Those who purchased loose cigarettes (vs packs) noticed HWLs less often (estimate -0.830, 95% CI -1.197 to -0.463, P<.001), whereas those who purchased loose bidis (vs bundles) read and looked closely at HWLs (estimate 0.646, 95% CI 0.013-1.279, P=.046), thought about the harms of bidi smoking (estimate 1.200, 95% CI 0.597-1.802, P<.001), and thought about quitting bidi smoking (estimate 0.871, 95% CI 0.282-1.461, P=.004) more often. Interview findings indicated lower exposure to HWLs among those who purchased loose cigarettes, primarily due to vendors distributing loose cigarettes without showing the original cigarette pack, storing them in separate containers, and consumers' preference for foreign-made cigarette brands, which often lack HWLs. While participants were generally aware of the contents of HWLs, many deliberately avoided them when purchasing loose cigarettes. In addition, they believed that loose cigarette purchases reduced the HWLs' potential to deliver consistent reminders about the harmful effects of cigarette smoking due to reduced exposure, an effect more common among those who purchased packs. Participants also noted that vendors, especially small ones, did not display statutory health warnings at their point of sale, further limiting exposure to warning messages. CONCLUSIONS: Survey and interview findings indicated that those who purchased loose cigarettes noticed HWLs less often. Loose purchases likely decrease the frequency of exposure to HWLs' reminders about the harmful effects of smoking, potentially reducing the effectiveness of HWLs.

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)0.006
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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