Statistical Analysis of Changes in Tobacco Consumption amid the COVID-19 Pandemic
Bibliographic record
Abstract
The author analyzed problems related to the prevalence of smoking and the need to combat the tobacco epidemic in Russia on the basis of current statistics and special surveys. Despite the fact that the number of smokers in Russia has been decreasing since 2009, there are new challenges for the authorities and society in their efforts to reduce the prevalence of smoking due to the emergence of new alternative tobacco and nicotine products, as well as changes in consumption habits due to the crisis caused by the COVID-19 pandemic. According to surveys carried out by the National Research University Higher School of Economics (from 2017 to 2020) and Rosstat (from 2011 to 2020), changes in tobacco consumption and smoking preferences have been identified, especially during the period of economic instability and the COVID-19 pandemic.The article explains the author’s position that despite the general decrease in the number of smokers (up to a quarter of adult population – according to data for 2020), their population is heterogeneous, and within it there were various processes, depending on the sex of the smoker, the intensity of smoking, preferences for nicotine-containing products. Firstly, over the period under review, the proportion of former smokers who have relinquished the habit has increased; secondly, the proportion of heavy smokers who used to consume a pack of cigarettes per day has decreased, and, conversely, the proportion of those who smoke about a quarter of a pack per day has increased. Smoking among women has two characteristics: lowering the age of onset of smoking to 19 years, along with increasing the daily consumption of cigarettes to an average of 12. Men, on the other hand, tend to reduce the daily consumption of cigarettes to 16 cigarettes on average. The proportion of smokeless tobacco products and electronic nicotine delivery systems is beginning to grow, but is still not a complete substitute for conventional cigarettes, which smoke about 95% of smokers. Finally, owing to the pandemic and crises in economy, the trend towards self-isolation has increased the number of people who smoke for the first time at a sufficiently mature age (30 years and older).Therefore, the results of the study revealed both certain patterns in tobacco consumption over the years preceding the pandemic and the impact of COVID-19 on social and economic processes involved in smoking that governance structures now need to take into account.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".