Web-Based Acceptance and Commitment Therapy Tobacco Cessation Program for Veterans With Mental Health Disorders: Adaptation and Usability Testing
Bibliographic record
Abstract
BACKGROUND: US veterans with mental health disorders have high rates of smoking and low rates of smoking cessation. OBJECTIVE: This study aims to focus on an adaptation of a web-based acceptance and commitment therapy (ACT) tobacco cessation intervention (Vet WebQuit) for veterans with mental health disorders who use tobacco and used a qualitative approach to test its usability (n=16). METHODS: Participants were asked to walk through the site during laboratory-based usability testing and "think aloud" about the features of the intervention. A trained facilitator used semistructured interview questions to assess participants' experiences with Vet WebQuit and obtain feedback on their impressions of the site. Qualitative analyses identified themes regarding participants' experiences with the intervention, usability concerns, and recommendations for improving Vet WebQuit. RESULTS: Overall, veterans found that the Vet WebQuit layout was simple and easy to navigate and use. Veterans reported that several features of the program were useful, including the quit plan, identification of triggers, content that targets mental health concerns (eg, dealing with anger), information on the health effects of smoking, tools for managing triggers (eg, urge surfing), and involving others in their quit plan. Veterans reported that particular features of the ACT approach for tobacco cessation were appealing to them, including the distinction between internal and external smoking triggers, the inclusion of the serenity prayer, and mindfulness exercises, which they could use as a tool reduce the intensity of cravings. Veterans reported wanting more information on the health aspects of smoking (ie, effects on breathing and lung capacity) as a way to motivate them to quit smoking. In addition, they suggested targeting specific mental health concerns that serve as triggers for smoking, including nightmares, boredom, and social isolation. CONCLUSIONS: Overall, results from this project identified important elements of ACT digital tobacco cessation interventions for veterans with mental health disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".