Health Care Professionals’ Clinical Skills to Address Vaping and e-Cigarette Use by Patients: Needs and Interest Questionnaire Study
Bibliographic record
Abstract
Background Widespread vaping and e-cigarette use is a relatively new phenomenon. Youth vaping peaked in 2019, with over 25% of high school students currently vaping. e-Cigarettes are used where smoking is not permitted or as an alternative smoking cessation treatment instead of Food and Drug Administration–approved options. Vaping and e-cigarette use has the potential to harm health, including causing adverse respiratory effects and nicotine addiction. Health care professionals need skills training to help their patients with this relatively new and evolving health problem. Objective The aim of this study is to understand health care professionals’ training needs in this subject area to determine the focus for web-based continuing education training. Methods We reviewed the literature on clinical aspects of vaping and e-cigarette use. Using the results and our experience in substance use continuing education, we created a list of key clinical skills and surveyed health care professionals about their training needs. We also asked about their interest in a list of related topics. We recruited individuals who completed our web-based courses on substance use, members of health care professional–related groups, and experts who had published an article on the subject. Half of the 31 health care professionals who completed the survey were physicians and the remainder were primarily nurses, social workers, and counselors. Participants self-identified as nonexperts (n=25) and experts (n=6) on vaping. Results Participants who were nonexperts on average agreed or strongly agreed that they needed training in each of 8 clinical skills (n=25; range 3.7-4.4 agreement out of 5). The top two skills were recommending treatments for patients (4.4 out of 5, SD 0.49) and evaluating and treating the health effects of vaping and e-cigarette use (4.4 out of 5, SD 0.50). Experts agreed on the importance of training for health care professionals in all skills but rated the need for training higher than nonexperts for each topic. Over half of the participating health care professionals (44%-80%) were interested in nearly all (9/10, 90%) vaping-related topics on a checklist. The topics participants were most interested in were the pros and cons of vaping versus smoking and the health effects of second- and third-hand vaping. Primary care physicians showed more interest in vaping-related topics than nonprimary care physicians (t13=2.17; P=.02). Conclusions This study confirmed gaps in health care professionals’ vaping-related clinical skills identified in the literature by identifying a perceived need for training in related skills and health care professionals’ interest in key topics related to vaping prevention and cessation. This study provides specific guidance on which clinical skills training is most needed and which topics are most interesting to health care professionals.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".