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Record W3007017256 · doi:10.1017/jsc.2020.2

Association of e-cigarette use and smoking cessation among Canadian young adult smokers: secondary analysis of data from a randomised controlled trial

2020· article· en· W3007017256 on OpenAlexaffabout
Arti Saxena, Neill Bruce Baskerville, John M. Garcia

Bibliographic record

VenueThe Journal of Smoking Cessation · 2020
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsPublic Health OntarioUniversity of Waterloo
Fundersnot available
KeywordsMedicineSmoking cessationOdds ratioRandomized controlled trialYoung adultLogistic regressionDemographyTobacco useInternal medicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Abstract Aims This study examined the reasons for e-cigarette (EC) use, changes in self-efficacy and association between EC use and cessation of tobacco among Canadian young adult smokers over a 6-month period. Methods A secondary analysis was conducted using data from a randomised controlled trial (RCT) of young adult Canadian smokers. EC exposure was defined as persistent, transient and non-use of ECs at baseline and follow-up. The association between EC exposure and cessation was examined using logistic regression and adjusting for co-variates. Results At 6-month follow-up, persistent EC use was associated with a lower cessation rate (13%) than transient (23%) or non-use (29%). After adjusting for covariates, non-use and transient use were associated with higher odds of cessation than persistent use (AOR = 3.23, 95% CI = 1.41–7.40, P < 0.01; AOR = 2.40, 95% CI = 1.01–5.58, P < 0.05). At 6-month follow-up, persistent users (68%) had high self-efficacy as compared to transient (15%) or non-use (12%). Top reasons for EC use included use as a quit aid (67%), perceived use as less harmful (52%) and taste (32%). Conclusions Among young adult Canadian smokers enrolled in a RCT of a cessation intervention, persistent and transient use of ECs was associated with a lower smoking cessation rate at 6 months.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.169
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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