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Record W2112604643 · doi:10.1093/pubmed/fdt030

Is roll-your-own tobacco substitute for manufactured cigarettes: evidence from Ireland?

2013· article· en· W2112604643 on OpenAlexfundno aff
Laura Cornelsen, Charles Normand

Bibliographic record

VenueJournal of Public Health · 2013
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersTerry Fox Research InstituteTrinity College Dublin
KeywordsConsumption (sociology)Tobacco controlTobacco useTobacco productTobacco harm reductionEnvironmental healthEconomicsMedicinePublic healthPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: When tax policies increase tobacco prices some smokers may switch to smoking cheaper roll-your-own (RYO) tobacco. To reduce the harm from smoking, this substitution effect should be avoided. This study analyses whether RYO tobacco is a substitute for manufactured cigarettes (MCs) in Ireland, a country with relatively high price for both products. METHODS: Data on duty-paid consumption of RYO tobacco from 1978 to 2011 are used to estimate the demand by applying seemingly unrelated regression and error correction models. Covariates include prices of tobacco in Ireland and in the UK, income and a variable describing tobacco-related health policies. RESULTS: We failed to find evidence of RYO tobacco being a substitute for MC due to price differences. However, an increase in incomes (1%) is associated with a reduction in the consumption of RYO tobacco (-0.4%), which can be due to substitution towards MCs in addition to quitting or cutting back. Also, an increase in the price of RYO tobacco (1%) is associated with a reduction in its consumption (-1%). CONCLUSIONS: Increasing prices via taxation is an effective way of reducing the consumption of RYO tobacco but due to associations between RYO tobacco smoking and lower incomes, these policies should be accompanied by measures aimed at helping smokers to quit.

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)0.001
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 score0.205
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.165
GPT teacher head0.384
Teacher spread0.219 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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