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Record W2509031467 · doi:10.1177/2010105816667137

Self-efficacy in treating tobacco use: A review article

2016· review· en· W2509031467 on OpenAlexaff
Rami A. Elshatarat, Mohammed Ibrahim Yacoub, Fadi Khraim, Zyad T. Saleh, Tareq Rateb Afaneh

Bibliographic record

VenueProceedings of Singapore Healthcare · 2016
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSelf-efficacySmoking cessationNicotineNicotine dependenceTobacco useIntervention (counseling)MedicineClinical efficacyCognitionNicotine replacement therapyClinical psychologyPsychologyPsychotherapistPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Globally, tobacco use continues to be a major health care concern. Despite strong recommendations to quit smoking, tobacco users are experiencing difficulties in quitting. The purpose of this integrative review is to discuss self-efficacy theory as an important behavioral therapy for treating tobacco use and nicotine dependence. Moreover, the paper proposes a literature-derived model that employs self-efficacy as a central component for treating tobacco use and nicotine dependence. Eleven relevant articles were included in this review. Self-efficacy has an important role in smoking cessation. Improving self-efficacy enhances the individual’s success in quitting tobacco use and preventing relapse. Moreover, incorporating self-efficacy as a cognitive behavioral intervention has shown various degrees of success for treating tobacco use and nicotine dependence. In order to offer guidance to health care providers assisting in quitting tobacco, a model that integrates self-efficacy as a central component of the quitting process is proposed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.405
Teacher spread0.302 · 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 designOther design
Domainnot available
GenreReview

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

Citations84
Published2016
Admission routes1
Has abstractyes

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