Hardcore smoking among daily smokers in male and female adults in 27 countries: a secondary data analysis of Global Adult Tobacco Surveys (2008-2014)
Bibliographic record
Abstract
Background"Hardcore smokers" (HCS) who do not want to quit make it more difficult for tobacco control efforts to further reduce smoking prevalence.We aimed to quantify the burden of HCS among daily smoking adult males and females in 27 countries. MethodsWe used Global Adult Tobacco Survey (GATS) data to estimate the prevalence of HCS ie, daily smokers who smoke within 30 minutes after waking up, smoke ≥10 cigarettes per day, have not made any quit attempts during previous 12 months or have no intention to quit at all during the coming 12 months.For each GATS country, we estimated sex-wise, weighted and age-adjusted prevalence of daily smoking and HCS. ResultsOverall weighted population prevalence (%) of HCS was highest in Greece (21.0), followed by Russia (13), Poland (9.4), Romania (9.0), and Ukraine (8.9) and lowest in Nigeria (0.4).Estimated number of HCS (in millions) was highest in China (35.8) followed by India (28.2),Russia (18.5),Indonesia (16.1) and lowest in Panama (0.03).The proportion (%) of daily smokers classified as HCS was highest in Greece (56.2%) followed by Russia (42.2%),Ukraine (37.2) and Poland (36.2) and lowest in Mexico (8.29).Overall, proportion of HCS was higher among males in all countries.However, in Greece, Russia, Romania, Ukraine and Poland both population prevalence of HCS among women and proportion of HCS among daily smoking women was higher than in other countries. ConclusionsAt the country-level, a higher daily smoking rates also suggest a higher proportion of HCS.Countries with greater burden of HCS pose greater challenges to tobacco control efforts specifically towards tobacco cessation interventions.Interventions to reduce tobacco use and smoking-related mortality may need to be altered in populations with high proportions of HCS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".