Digital Smoking Cessation Preferences of Predominately Low-Income and Latino Residents of the San Joaquin Valley in California: Qualitative Study
Bibliographic record
Abstract
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".