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Record W2502895934 · doi:10.1136/bmjopen-2016-011076

Determinants of regular smoking onset in South Africa using duration analysis

2016· article· en· W2502895934 on OpenAlexfundno aff
Nicole Vellios, Corné van Walbeek

Bibliographic record

VenueBMJ Open · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersMcMaster UniversityBill and Melinda Gates FoundationAfrican Capacity Building FoundationAmerican Cancer Society
KeywordsMedicineExciseTobacco controlSocioeconomic statusDemographySmoking prevalenceEnvironmental healthQuit smokingCigarette smokingLow and middle income countriesDeveloping countryPublic healthSmoking cessationPopulation

Abstract

fetched live from OpenAlex

OBJECTIVES: South Africa has achieved significant success with its tobacco control policy. Between 1994 and 2012, the real price of cigarettes increased by 229%, while regular smoking prevalence decreased from about 31% to 18.2%. METHODS: Cigarette prices and socioeconomic variables are used to examine the determinants of regular smoking onset. We apply duration analysis techniques to the National Income Dynamics Study, a nationally representative survey of South Africa. RESULTS: We find that an increase in cigarette prices significantly reduces regular smoking initiation among males, but not among females. Regular smoking among parents is positively correlated with smoking initiation among children. Children with more educated parents are less likely to initiate regular smoking than those with less educated parents. Africans initiate later and at lower rates than other race groups. CONCLUSIONS: As the tobacco epidemic is shifting towards low-income and middle-income countries, there is an increasing urgency to perform studies in these countries to influence policy. Higher cigarette excise taxes, which lead to higher retail prices, reduce smoking prevalence by encouraging smokers to quit and by discouraging young people from starting smoking.

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)0.000
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 score0.008
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.134
GPT teacher head0.414
Teacher spread0.280 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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