Health Care Professionals’ Clinical Skills to Address Vaping and e-Cigarette Use by Patients: Needs and Interest Questionnaire Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».