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No surge in illicit cigarettes after implementation of menthol ban in Nova Scotia

2018· article· en· W2896242326 on OpenAlexaboutno aff
Michał Stokłosa

Bibliographic record

VenueTobacco Control · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaMentholTobacco industryEnforcementEnvironmental healthJurisdictionTobacco controlMedicineBusinessAdvertisingPolitical scienceGeographyLawPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: In May 2015, the Canadian province of Nova Scotia became the first jurisdiction in the world to ban menthol cigarettes specifically. The tobacco industry warned that 'the primary effect of this law will be to increase the illegal tobacco market in Nova Scotia'. This is the first attempt to examine the impact of the menthol ban on trends in illicit cigarettes. DATA AND METHODS: Data on the number of illicit cigarettes seized in Nova Scotia covering the period from 2007/2008 to 2017/2018 was obtained from the Provincial Tax Commission. Data from before and after the ban are compared. RESULTS: According to the local authorities, while the enforcement efforts in Nova Scotia have not declined, the number of seized illicit cigarettes declined significantly, from >60 000 cartons in 2007/2008 to <10 000 cartons in 2017/2018. Since the menthol ban, the seizure volume remained stable, with no statistically significant difference in the number of cigarettes seized before and after the menthol ban (t=-0.71, p=0.55). There were only a few small seizures of menthol cigarettes in the year following the ban, after which there have been no further seizures of menthol cigarettes. DISCUSSION: Contrary to the tobacco industry's assertions, there was no surge in illicit cigarettes after the 2015 ban on menthol cigarette sales in Nova Scotia. Credible, industry-independent evidence on illicit cigarette trade is desperately needed to support the implementation of tobacco control policies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.999

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)0.0020.000

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 head0.015
GPT teacher head0.313
Teacher spread0.298 · 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.

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

Citations38
Published2018
Admission routes1
Has abstractyes

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