Strengths and Limitations of Web-Based Cessation Support for Individuals Who Smoke, Dual Use, or Vape: Qualitative Interview Study
Notice bibliographique
Résumé
BACKGROUND: Tobacco use has shifted in recent years, especially with the introduction of e-cigarettes. Despite the current variable and intersecting tobacco product use among tobacco users, most want to quit, which necessitates cessation programs to adapt to these variable trends (vs focusing on combustible cigarettes alone). The use of web-based modalities for cessation support has become quite popular in recent years and has been compounded by the COVID-19 pandemic. Therefore, understanding the current strengths and limitations of existing programs to meet the needs of current various tobacco users is critical for ensuring the saliency of such programs moving forward. OBJECTIVE: The purpose of this study was to understand the strengths and limitations of web-based cessation support offered through QuitNow to better understand the needs of a variety of end users who smoke, dual use, or vape. METHODS: Semistructured interviews were conducted with 36 nicotine product users in British Columbia. Using conventional content analysis methods, we inductively derived descriptive categories and themes related to the strengths and limitations of QuitNow for those who smoke, dual use, or vape. We analyzed the data with the support of NVivo (version 12; QSR International) and Excel (Microsoft Corporation). RESULTS: Participants described several strengths and limitations of QuitNow and provided suggestions for improvement, which fell under 2 broad categories: look and feel and content and features. Shared strengths included the breadth of information and the credible nature of the website. Individuals who smoke were particularly keen about the site having a nonjudgmental feeling. Moreover, compared with individuals who smoke, individuals who dual use and individuals who vape were particularly keen about access to professional quit support (eg, quit coach). Shared limitations included the presence of too much text and the need to create an account. Individuals who dual use and individuals who vape thought that the content was geared toward older adults and indicated that there was a lack of information about vaping and personalized content. Regarding suggestions for improvement, participants stated that the site needed more interaction, intuitive organization, improved interface esthetics, a complementary smartphone app, forum discussion tags, more information for different tobacco user profiles, and user testimonials. Individuals who vape were particularly interested in website user reviews. In addition, individuals who vape were more interested in an intrinsic approach to quitting (eg, mindfulness) compared with extrinsic approaches (eg, material incentives), the latter of which was endorsed by more individuals who dual use and individuals who smoke. CONCLUSIONS: The findings of this study provide directions for enhancing the saliency of web-based cessation programs for a variety of tobacco use behaviors that hallmark current tobacco use.
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 | 0,023 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».