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Record W2089254742 · doi:10.3109/09687637.2011.562935

Understanding the motivations of contraband tobacco smokers

2011· article· en· W2089254742 on OpenAlexfundno aff
Breanna Pellegrini, Tim R. L. Fry, Campbell Aitken

Bibliographic record

VenueDrugs Education Prevention and Policy · 2011
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersHealth Canada
KeywordsTobacco useConsumption (sociology)AdvertisingEnvironmental healthMedicineBusiness

Abstract

fetched live from OpenAlex

Aim: This study explored the motivations behind illicit tobacco use in Australia. A key focus was to investigate the hypothesis that the primary motivation for illicit tobacco use is its low cost in comparison to the price of legal tobacco.Methods: An Australian tobacco usage telephone survey was conducted in 2007. Illicit tobacco smokers completed a longer version of the questionnaire, with questions relating to illicit tobacco usage and perceptions.Findings: Of the current smokers of illicit tobacco surveyed, almost half would consider increasing illicit tobacco consumption if the cost of legal tobacco were to increase to four times the price of illicit tobacco, although others stated consumption would remain the same regardless of such a price change. Almost all former smokers of illicit tobacco claimed that price did not influence the decision to stop smoking illicit tobacco.Conclusions: Some illicit tobacco smokers appear to be sensitive to the price of tobacco products, but price is not always an underlying motivator. Personal preference and supply also influence illicit tobacco consumption. The findings suggest that reducing the availability of illicit tobacco would be a useful strategy for combating the growing illicit tobacco problem.

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 score0.404
Threshold uncertainty score0.217

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 head0.119
GPT teacher head0.361
Teacher spread0.242 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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