Electronic Cigarette in Saudi Arabia: An Online Survey
Bibliographic record
Abstract
Background: E-cigarettes have been recently used to quit smoking. Their use became popular regardless of the fact that WHO considered them as a source of toxic fumes. Data about their safety is not yet confirmed, but major tobacco companies are advertising and producing them. Conducting clinical trials of these devices is challenging. Purpose: To measure e-cigarette awareness in Saudi Arabia, and study their use among smokers and non-smokers. Methodology: An electronic survey (Part of the validated WHO Global Adult Tobacco Survey) was used to reach participants through many internet communication applications. Microsoft Excel® was used to enter and analyze the data. Results: 3027 participants were included in the analysis. Most of the participants were males (67.7%), aged between 18-40 years (73%), Saudi national (96 %), having a university degree (56.9%) and employed (56.9%). Awareness of e-cigarettes was high, as more than three quarters of respondents (82.5%) had heard about e-cigarettes. Less than half (42.5%) of those respondents who were aware of ecigarettes have bought it or have seen anyone buying it. Among those respondents who were aware of ecigarettes, one third (33.5%) had tried it. Of those who didn’t ever smoke e-cigarettes, only (17.4%) were willing to try it in the current time. Less than one quarter of the respondents (22.3%) were smoking regular cigarettes. Of those, around two thirds (62.9%) were trying to quit smoking regular cigarettes, and among those, only (18.2%) were using e-cigarettes to help them do so. Only (8.8%) of the respondents believed that e-cigarettes is not harmful. Conclusion: Smoking e-cigarettes is popular in Saudi Arabia, especially in non-smokers. Such popularity may “re-normalize” smoking, and lead to an increase in an “already alarming” smoking prevalence and addiction, especially to youngsters or at least a slowing down of the rate of decline.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".