Digital Smoking Cessation Preferences of Predominately Low-Income and Latino Residents of the San Joaquin Valley in California: Qualitative Study
Notice bibliographique
Résumé
Background: Although rates of tobacco use in California have declined overall, adults in the San Joaquin Valley (SJV), particularly Hispanic or Latinos ("Latinos"), have disproportionately high rates of tobacco use, tobacco-related illness, and mortality. Residents of the SJV also have limited access to cessation support services and need accessible, nonclinical alternatives. Given high smartphone use rates among Latinos and residents of rural communities, digital health tools may present an accessible approach to expand cessation support. Objective: This study explored tobacco use behaviors, cessation experiences, and views about digital cessation tools for tobacco cessation among SJV residents. The secondary objective was to assess the appeal, usability, and necessary adaptations of 2 existing digital smoking cessation tools-a smoking cessation app and a social media-based cessation intervention. Methods: Through an SJV-based academic-community partnership, we recruited 29 predominantly Latino adults who reported current smoking. We conducted 4 focus group discussions to explore tobacco use and cessation experiences and preferences for smoking cessation tools: 1 in-person in English, 1 online in English, and 2 online in Spanish. Subsequently, 9 participants from the focus group discussions completed individual, in-depth interviews where they viewed videos describing 2 digital smoking cessation tools-a cessation app and a social media cessation intervention-to assess their appeal and usability. Focus groups and interviews were recorded, transcribed, and analyzed to identify themes. Results: Overall, 82.1% (23/28) had made a quit attempt in the past year, and most intended to quit smoking in the next 6 months, with 11.1% (3/27) never expecting to quit. Most participants were motivated to quit despite experiencing barriers, and they emphasized the need for culturally tailored digital cessation tools to help overcome the barriers to quitting smoking. They preferred interventions that integrated culturally relevant content reflecting lived experiences, featured language-concordant communications, and provided social supports, such as chat rooms for peer connection. Participants reported polyuse of tobacco with other substances, including cannabis, which may need to be addressed when delivering smoking cessation interventions. While participants appreciated the app's private interface and comprehensive curriculum, they preferred the social media-based program for its engaging design, despite privacy concerns. Preferences for specific interventions varied by age and digital literacy. Participants also expressed preference for material rewards to incentivize the use of digital health tools to quit smoking. Conclusions: This sample of predominantly Latino adults from the SJV expressed favorable interest in digital cessation support, yet existing tools require adaptation to improve cultural relevance, accessibility, usability, and privacy concerns. Participants emphasized language-concordant services, representation from people with lived experience, and community-building features. While digital interventions were well received, privacy concerns and digital literacy barriers must be addressed to enhance engagement.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».