Gender based Differences in Tobacco Cessation initiatives: A Bibliometric Review Analysis
Bibliographic record
Abstract
Introduction: As per the Global Action to end smoking report in 2022, India had 253 million tobacco users, ranking second globally. According to NFHS-5 (2019-21), tobacco use was highest among males aged 50-64 (52.8%). Men and women exhibited similar quit attempts, but women were 31% less likely to succeed due to factors like post-cessation weight gain and Varenicline response differences. This study assesses global research and academic literature trends on gender differences in tobacco cessation. Methods: Bibliographic data were collected from PubMed using search terms related to tobacco cessation and gender differences. 483 publications were analyzed using VOSviewer 1.6.19 and RStudio 4.4.0. Key data included publication years, authors, country, keywords, and citation count. Co-authorship and keyword networks were visualized to identify key themes and collaborations. Results: Publication growth was slow until 2006 but rose sharply from 2012 onward, peaking between 2018-2022 before declining in 2023-24. The USA and Canada led research output, with the University of Michigan and University of California as top contributors. “Tobacco use cessation devices” gained prominence from 2015-2021, while “smoking cessation” and “tobacco products” were terms frequently used from 2017-2022. Conclusion: This bibliometric analysis highlights research trends in tobacco cessation, emphasizing the need for intervention-focused studies to address gender disparities globally. While descriptive studies remain valuable, intervention research is needed to bridge gender gaps in tobacco cessation outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.030 | 0.047 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".