Utilization and Experiences of Using Quit Now, a Nicotine and Tobacco Smoking Cessation Website: Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: British Columbia residents have access to a program called QuitNow that provides behavioral support and information about pharmacotherapy to nicotine and tobacco users. Web- or computer-based smoking cessation programs have been shown to yield an abstinence rate about 1.5 times higher when compared to a control. Although quantitative evidence reveals significant promise for web-based services like QuitNow, there is very little qualitative evidence available. Understanding website utilization and the experiences of end users is key to contextualizing the effectiveness of web-based cessation services and providing directions for enhancing these services. OBJECTIVE: This qualitative interview study aims to delve into users' utilization and experiences of QuitNow, which is supplemented by Google Analytics data. METHODS: We interviewed 10 QuitNow users using semistructured interviews to understand what they liked the most and the least about QuitNow. We transcribed these interviews and conducted an inductive thematic analysis using NVivo (QSR International) software to extract common themes about user experiences. We also gathered utilization metrics via Google Analytics (n=13,856 users) to understand which aspects of QuitNow were used the most and which were used the least during the study period. RESULTS: Thematic analysis yielded four major themes: (1) barriers to information access reduce opportunities to take action, (2) lack of clarity around pharmacological options is discouraging, (3) hearing from others is an important part of the journey, and (4) recognizing own agency throughout the quit process. These themes provided context and support for the Google Analytics data, which showed that end user activity, measured by indicators such as page views and average time spent on each page, was highest on pages about how to quit (10,393 page views), pharmacology information (1999 page views), and the community forum (11,560 page views). CONCLUSIONS: Results of this study point to several important implications for improving the website, as well as directions for enhancing cessation support services in general.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".