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Tobacco industry response to menthol cigarette bans in Alberta and Nova Scotia, Canada

2016· article· en· W2500283069 on OpenAlexaffabout
Jennifer Brown, Teresa DeAtley, Kevin Welding, Robert Schwartz, Michael Chaiton, Deirdre Lawrence Kittner, Joanna E Cohen

Bibliographic record

VenueTobacco Control · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsHamilton Health SciencesPublic Health OntarioUniversity of TorontoOntario Tobacco Research Unit
FundersPhilip Morris International
KeywordsMentholTobacco industryNova scotiaTobacco controlEnvironmental healthPackaging and labelingJurisdictionBusinessMedicinePublic healthAdvertisingPolitical scienceGeographyMarketingLaw

Abstract

fetched live from OpenAlex

Menthol cigarettes are associated with increased initiation and progression to regular smoking and decreased likelihood of smoking cessation.1–8 Menthol smokers are more likely to be women and adolescents in several countries.9 The Conference of the Parties to the Framework Convention on Tobacco Control recommend that Parties regulate ingredients that make cigarettes more palatable, including flavouring substances like menthol.10 The Canadian province Nova Scotia became the first jurisdiction to implement a ban on menthol tobacco products in May 2015, and the province of Alberta followed in September 2015.11 These regulations extended existing provincial bans on the sale of flavoured tobacco products to include menthol flavoured tobacco products, with the exception of pipe tobacco and some cigars. Additional Canadian provinces, Brazil, Ethiopia, Turkey and the European Union have passed regulations to ban menthol tobacco products.11 As jurisdictions (including cities, states/provinces and countries) consider bans on menthol tobacco products, real-life contextual data on the industry response to such bans can be helpful in formulating effective bans. For example, when misleading descriptors on tobacco packaging such as ‘light’ and ‘low tar’ were prohibited, the tobacco industry continued to communicate those same misleading health messages to the consumer using colour or other descriptors.12 ,13 Industry tactics to undermine the effectiveness of health warnings on tobacco packaging have included the use of promotional packaging and altered pack size.14 ,15 Drawing on a sample of cigarette packs …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.293
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 teacher head, 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

Citations42
Published2016
Admission routes2
Has abstractyes

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