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Intended and unintended effects of restrictions on the sale of cigarillos to youth: evidence from Canada

2014· article· en· W2122759998 on OpenAlexaffabout
Hai V. Nguyen, Paul Grootendorst

Bibliographic record

VenueTobacco Control · 2014
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessAdvertising

Abstract

fetched live from OpenAlex

BACKGROUND: Youth consumption of cigarillos (ie, little cigars) has increased markedly in recent years. In July 2010, the Canadian government banned the sale of flavoured cigarillos and required unflavoured cigarillos to be sold in packs of at least 20 units. This paper assesses changes in young persons' use of cigarillos and regular cigars, which are potential substitutes, following the policy. METHODS: To investigate of the change in cigar smoking following the policy, we constructed a segmented regression model that allowed the policy to change the height and the slope of the trend in the outcome variables. The model was estimated using data from the 2007-2011 Canadian Tobacco Use Monitoring Surveys. RESULTS: We obtained visual and regression-based evidence that use of cigarillos among youth declined following the policy. We also found a small, gradual increase in their use of regular cigars, possibly due to their compensatory switching from cigarillos to regular cigars. Overall, there was a net reduction in cigar use among youth after the intervention. INTERPRETATION: The policy achieved its goal of reducing youth's consumption of cigarillos, but may have an unintended consequence of increasing their use of regular cigars. Policymakers should address the possibility that youth switch to regular cigars in response to restricted access to cigarillos. Possible ways of discouraging this substituting behaviour include extending the ban to cover all flavoured cigars and mandating a minimum pack size for all cigars, or raising taxes on flavoured cigars.

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)0.001
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 score0.284
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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