The Frequency of Use and Harm Perception of Heated Tobacco Products (HTPs): The 2019 Cross-Sectional Survey among Medical Students from Poland
Bibliographic record
Abstract
Heated tobacco products (HTPs) are devices for generating a nicotine aerosol by heating the tobacco sticks. This study aimed to assess (1) the prevalence of HTP and tobacco cigarette usage among medical students, (2) to characterize smoking habits and (3) to assess students’ awareness and opinions about HTPs. A cross-sectional survey on the frequency and attitudes toward cigarettes, e-cigarettes and HTP use was performed between 2019–2020 at the Medical University of Silesia in Katowice (Poland). The data were obtained from 1344 students aged 21.8 ± 1.9 years (response rate: 66.9%). Current traditional tobacco use was 13.2%, e-cigarettes use 3.5%, and HTP use 2.8% of students. Duration of use was shorter among HTPs users comparing to cigarette smokers (p < 0.001) although the number of tobacco sticks used daily was similar (p = 0.1). Almost 30% of respondents have ever tried HTPs. HTPs were considered safe by 5.3% of respondents (43.2% of HTP users vs. 3.9% of non-HTP users, p < 0.001). HTP users were more likely to report that heating tobacco is not addictive (odds ratio (OR) = 8.9, 95% confidence interval (CI): 1.8–45.8) and disagreed with a public ban on HTP use (OR = 4.9, 95%CI: 2.5–9.8). Among students, HTP use was less popular than cigarette smoking, but awareness of their presence is widespread.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".