Prevalence and Severity of Nicotine Dependence in India: A Systematic Review and Meta-Analysis Protocol
Bibliographic record
Abstract
Abstract Background Despite the high prevalence on tobacco use in India, evidence on nicotine dependence and its severity across the demographic groups is limited. Therefore, this review aims to estimate the pooled prevalence and severity of nicotine dependence among tobacco users in India, stratified by age, gender, type of tobacco, and rural–urban setting. Methods The protocol has been prospectively registered in PROSPERO (ID: CRD420251133993) and adheres to the PRISMA-P 2015 guidelines. Comprehensive literature searches will be conducted across Medline, Cochrane Library, Embase, Scopus, and grey literature sources. Eligible studies will include community-based research reporting nicotine dependence assessed using the Fagerström Test for Nicotine Dependence (FTND) or FTND-ST for smokeless tobacco. Two independent reviewers will carry out study selection, data extraction, and risk-of-bias assessment using the JBI checklist and the Newcastle–Ottawa Scale. Pooled prevalence will be calculated using a random-effects model, supplemented by subgroup analyses, sensitivity analyses, and meta-regression. Publication bias will be assessed through Deviation from Expected Inverse Variance (DOI) plots for graphical asymmetry evaluation or by funnel plots, alongside the Luis Furuya–Kanamori (LFK) index, where values within ±1 suggest no asymmetry, ±1 to ±2 indicate minor asymmetry, and >±2 represent major asymmetry. The overall certainty of evidence will be appraised using the GRADE framework. Discussion By generating robust estimates of nicotine dependence and its severity among tobacco users in India, this review will address a significant evidence gap in the field. The findings will be valuable for policymakers, public health professionals, and clinicians, enabling them to design context-specific tobacco control strategies, enhance cessation services, and develop targeted interventions. Such measures will contribute meaningfully to achieving Sustainable Development Goals 3 and 3.a, which aim to reduce premature mortality from non-communicable diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.088 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.024 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.008 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".