Medical Trainees’ Knowledge and Attitudes Towards Electronic Cigarettes and Hookah: A Multinational Survey Study
Bibliographic record
Abstract
BACKGROUND: The rising prevalence of electronic cigarette (e-cigarette) and hookah use among youth raises questions about medical trainees' views of these products. We aimed to investigate medical trainees' knowledge and attitudes toward e-cigarette and hookah use. METHODS: We used data from a large cross-sectional survey of medical trainees in Brazil, the United States, and India. We investigated demographic and mental health aspects, history of e-cigarettes and tobacco use, knowledge and attitudes toward e-cigarettes and hookah, and sources of information on e-cigarettes and hookah. Although all medical trainees were eligible for the original study, only senior students and physicians-in-training were included in the present analysis. RESULTS: Of 2,036 senior students and physicians-in-training, 27.4% believed e-cigarette use to be less harmful than tobacco smoking. As for hookah use, 14.9% believed it posed a lower risk than cigarettes. More than a third of trainees did not acknowledge the risks of passive e-cigarette use (42.9%) or hookah smoking (35.1%). Also, 32.4% endorsed e-cigarettes to quit smoking, whereas 22.5% felt ill equipped to discuss these tobacco products with patients. Fewer than half recalled attending lectures on these topics, and their most common sources of information were social media (54.5%), Google (40.8%), and friends and relatives (40.3%). CONCLUSIONS: Medical trainees often reported incorrect or biased perceptions of e-cigarettes and hookah, resorted to unreliable sources of information, and lacked the confidence to discuss the topic with patients. An expanded curriculum emphasis on e-cigarette and hookah use might be necessary because failing to address these educational gaps could risk years of efforts against smoking normalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".