Strengths and Limitations of Web-Based Cessation Support for Individuals Who Smoke, Dual Use, or Vape: Qualitative Interview Study
Bibliographic record
Abstract
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.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".