Exploring General Practitioners’ Knowledge, Attitudes, and Practices towards E-Cigarette Use/Vaping in Children and Adolescents: A Pilot Cross-Sectional Study in Sydney
Bibliographic record
Abstract
(1) Background: The increasing use of e-cigarettes/vaping in children and adolescents has been recognised as a global health concern. We aim to explore the Knowledge, Attitude, and Practice of General Practitioners (GPs) in Sydney regarding the use of e-cigarettes in children and adolescents and identify the barriers to addressing this issue. (2) Methods: This pilot study was a cross-sectional study conducted using an electronic questionnaire with a Likert scale and free-text responses. (3) Results: Fifty-three GPs participated in the study (male = 24 and female = 29) with a mean age of 50 ± 5.5 years. There was strong agreement (mean 4.5) about respiratory adverse effects and addictive potential. However, there was less awareness of cardiac side effects and the occurrence of burns. There is a lack of conversation about e-cigarettes in GP practice and a deficit of confidence in GPs regarding managing e-cigarette use in children and adolescents. (4) Conclusions: Our pilot study has shown that GPs are somewhat knowledgeable about the potential adverse effects of the use of e-cigarettes in children and adolescents, though there is a lack of knowledge of the complete spectrum of adverse effects and more importantly, there is a paucity of a structured approach to discuss the use of e-cigarettes with children and adolescents, and there is a low level of confidence in addressing these issues. There is a need for educational interventions for GPs to increase awareness of the potential adverse effects of using e-cigarettes and build confidence in providing management to children and adolescents regarding the use of e-cigarettes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".