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Record W2030222359 · doi:10.1186/1471-2458-8-129

Reasons for not using smoking cessation aids

2008· article· en· W2030222359 on OpenAlexaboutno aff
Beatrice Groß, Leonie S. Brose, Anja Schümann, Sabina Ulbricht, Christian Meyer, Henry Völzke, Hans‐Jürgen Rumpf, Ulrich John

Bibliographic record

VenueBMC Public Health · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSmoking cessationBiostatisticsPsychological interventionPublic healthPopulationQuarter (Canadian coin)PsychiatryDemographyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Few smokers use effective smoking cessation aids (SCA) when trying to stop smoking. Little is known why available SCA are used insufficiently. We therefore investigated the reasons for not using SCA and examined related demographic, smoking behaviour, and motivational variables. METHODS: Data were collected in two population-based studies testing smoking cessation interventions in north-eastern Germany. A total of 636 current smokers who had never used SCA and had attempted to quit or reduce smoking within the last 12 months were given a questionnaire to assess reasons for non-use. The questionnaire comprised two subscales: "Social and environmental barriers" and "SCA unnecessary." RESULTS: The most endorsed reasons for non-use of SCA were the belief to be able to quit on one's own (55.2%), the belief that help is not necessary (40.1%), and the belief that smoking does not constitute a big problem in one's life (36.5%). One quarter of all smokers reported that smoking cessation aids are not helpful in quitting and that the aids cost too much. Smokers intending to quit agreed stronger to both subscales and smokers with lower education agreed stronger to the subscale "Social and environmental barriers". CONCLUSION: Main reasons for non-use of SCA are being overly self-confident and the perception that SCA are not helpful. Future interventions to increase the use of SCA should address these reasons in all smokers.

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 score0.056
Threshold uncertainty score0.387

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 head0.215
GPT teacher head0.394
Teacher spread0.179 · 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

Citations50
Published2008
Admission routes1
Has abstractyes

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