Reasons for not using e-cigarettes among adults who smoke: findings from the 2019–2021 Euromonitor International’s Voice of the Consumer: Nicotine Survey in 20 countries
Bibliographic record
Abstract
BACKGROUND: As e-cigarette use rates continue to increase globally, people in different countries have developed varying perceptions of e-cigarette use. Understanding these perceptions could potentially help policymakers develop regulations that align with their tobacco control objectives. Existing research on the reasons for not using e-cigarettes among adults who smoke cigarettes has been limited to specific countries. This study broadens the scope and reports reasons for not using e-cigarettes among adults who smoke cigarettes in 20 leading nicotine markets worldwide. METHODS: We performed a secondary analysis of data from the 2019-2021 Euromonitor International's Voice of Consumer: Nicotine Survey. The study sample consisted of adults who had never used e-cigarettes or had used them more than one year ago, and who currently smoked combustible cigarettes monthly or more often in 20 leading nicotine-consuming countries, including Canada, China, Czech Republic, France, Germany, Greece, Israel, Italy, Japan, Kazakhstan, Netherlands, Poland, Romania, Russia, Slovakia, South Korea, Spain, Ukraine, the United Kingdom, and the United States. Reasons for not using e-cigarettes were measured with a multiple-response question. Weighted percentages for each reason overall and by age, sex, and country were reported. RESULTS: The top reasons for not using e-cigarettes among adults who smoked cigarettes across the 20 countries were "never considered/not enough information," "inauthentic/not 'the real thing,'" and "too expensive," closely followed by "safety concerns." The top reasons were similar in most countries, with some variations. Greece had "do not want to quit smoking" and France had "prolonging addiction/substituting to another format" among the top reasons. In Poland, South Korea, Romania, and Slovakia, "unaware of the product" was one of the top reasons. CONCLUSIONS: Tobacco control practitioners and policymakers can use the findings on reasons for not using e-cigarettes both domestically and globally to inform public health campaigns and policies that align with the specific tobacco control objectives in each country to ultimately reduce the burden of tobacco-related disease.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".