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Smoking cessation interventions in South Asian Region: a systematic scoping review

2022· other· en· W6977388525 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
Fundersnot available
KeywordsSmoking cessationPsychological interventionCINAHLTobacco controlGrey literatureSystematic reviewMEDLINEContext (archaeology)

Abstract

fetched live from OpenAlex

Abstract Background Cigarette smoking is one of the most preventable causes of morbidities and mortalities. Since 2005, the World Health Organization Framework Convention for Tobacco Control (WHO-FCTC) provides an efficient strategic plan for tobacco control across the world. Many countries in the world have successfully reduced the prevalence of cigarette smoking. However, in developing countries, the prevalence of cigarette smoking is mounting which signifies a need of prompt attention. This scoping review aims to explore the extent and nature of Smoking Cessation (SmC) interventions and associated factors in South Asian Region (SAR) by systematically reviewing available recently published and unpublished literature. Methods The Joanna Briggs Institute (JBI) framework frames the conduct of this scoping review. PubMed, EBSCO CINAHL Complete, Cochrane Library, ProQuest Dissertation and Theses, and local websites as well as other sources of grey literature were searched for relevant literature. In total, 573 literature sources were screened. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram, finally, 48 data sources were included for data extraction and analysis. We analyzed the extracted SmC interventions through the FCTC. Factors that affect smoking cessation interventions will be extracted through manual content analysis. Results Regarding FCTC recommended smoking cessation strategies (articles), most of the articles were either neglected or addressed in a discordant way by various anti-smoking groups in SAR. Key barriers that hamper the effectiveness of smoking cessation interventions included lack of awareness, poor implementation of anti-smoking laws, and socio-cultural acceptance of tobacco use. Conversely, increased levels of awareness, through different mediums, related to smoking harms and benefits of quitting, effective implementation of anti-smoking laws, smoking cessation trained healthcare professionals, support systems, and reluctance in the community to cigarette smoking were identified as facilitators to smoking cessation interventions. Conclusion The ignored or uncoordinated FCTC’s directions on smoking cessation strategies have resulted in continued increasing prevalence of cigarette smoking in developing countries, especially SAR. The findings of this review highlight the need for refocusing the smoking cessation strategies in SAR. Strengths The review was conducted by a team of expert comprising information specialists, and senior professors bringing rich experience in systematic and scoping reviews. Every effort was made to include all available literature sources addressing cigarette SmC and associated factors in SAR. The review findings signal the need and direction for more SmC efforts in SAR which may contribute to development of effective policies and guidelines for the control of smoking prevalence. Limitations Despite efforts, potentially relevant records may have been missed due to unpublished or inaccessible articles, unintended selection bias, or those published in local languages, etc. Moreover, the exclusion of literature on under 18 participants and mentally ill smokers may limit the generalizability of findings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0140.014
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.133
GPT teacher head0.357
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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