Prevalence of tobacco and nicotine products consumption among the population of Ukraine and the world (review)
Bibliographic record
Abstract
Objective: To analyze scientific data on the prevalence of tobacco and nicotine product consumption among the population of Ukraine and the world. Materials and Methods: An analytical review of scientific publications was conducted using bibliographic databases of the National Scientific Medical Library of Ukraine, WHO’s Medical Information Network, the National Library of Medicine (USA), the National Institutes of Health (USA), Directory of Open Access Journals, BioMed Central, FREE MEDICAL JOURNALS, ScienceDirect, The BMJ, among others. Scientometric, logical, and systematic analysis methods were employed. Overview: Currently, there are over 1.25 billion adult tobacco consumers worldwide. It is known that 84.0% of current smokers reside in developing countries, notably in Eastern Europe, Asia, Latin America, North Africa, and some island nations of Oceania. In several countries, such as the USA, Canada, Brazil, Australia, the United Kingdom, Norway, and Italy, the smoking rate among men is at its lowest due to strict regulatory measures. Ukraine ranks first in Europe for the number of male smokers, with every second adult Ukrainian smoking, totaling 15.5 million smokers in the country. Smoking among adolescents remains a serious issue in many regions worldwide, especially in countries with weak anti-smoking policies. Recent WHO-initiated surveys indicate a decrease in the prevalence of traditional smoking methods among Ukrainian adolescents; however, the use of alternative smoking products, such as e-cigarettes, hookah, and heated tobacco devices, poses a new public health challenge in Ukraine. Conclusions: The analysis of scientific literature indicates that smoking is a serious public health issue globally. Amid decreasing levels of traditional tobacco smoking prevalence among the child population of Ukraine, there is an observed increase in the prevalence of alternative nicotine products, aligning with global trends. It was noted that the military aggression against Ukraine may have contributed to an increase in smoking prevalence among the population. KEYWORDS: adult population, child population, smoking prevalence, alternative smoking products, scientific literature
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".