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Record W21655144 · doi:10.3390/ijerph8051671

Les Trentes Glorieuses

2013· article· en· W21655144 on OpenAlexfundno aff
C Kevin

Bibliographic record

VenuePublications Docs-en-stock.com · 2013
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Cancer InstituteCanadian Institutes of Health ResearchCancer Research UK
KeywordsComputer science

Abstract

fetched live from OpenAlex

This paper examines how price minimizing behaviors impact efforts to stop smoking. Data on 4,988 participants from the International Tobacco Control Policy Evaluation (ITC) Four-Country Survey who were smokers at baseline (wave 5) and interviewed at a 1 year follow-up were used. We examined whether price minimizing behaviors at baseline predicted: (1) cessation, (2) quit attempts, and (3) successful quit attempts at one year follow up using multivariate logistic regression modeling. A subset analysis included 3,387 participants who were current smokers at waves 5 and 6 and were followed through wave 7 to explore effects of changing purchase patterns on cessation. Statistical tests for interaction were performed to examine the joint effect of SES and price/tax avoidance behaviors on cessation outcomes. Smokers who engaged in any price/tax avoidance behaviors were 28% less likely to report cessation. Persons using low/untaxed sources were less likely to quit at follow up, those purchasing cartons were less likely to make quit attempts and quit, and those using discount cigarettes were less likely to succeed, conditional on making attempts. Respondents who utilized multiple behaviors simultaneously were less likely to make quit attempts and to succeed. SES did not modify the effects of price minimizing behaviors on cessation outcomes. The data from this paper indicate that the availability of lower priced cigarette alternatives may attenuate public health efforts aimed at to reduce reducing smoking prevalence through price and tax increases among all SES groups.

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 score0.084
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.335
Teacher spread0.285 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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