Interactive Conversational Agents for Cigarette-Smoking and Vaping Cessation: A Mixed-Methods Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interactive conversational agents, also known as chatbots, have the potential to increase the success rate of digital technology interventions to promote healthy behaviors. However, due to their newness and limited use, little is known about their integration, usefulness, and effectiveness in promoting smoking and vaping cessation. The aim of this mixed-methods systematic review was to assess the effectiveness and characteristics of current interactive conversational agents in promoting and supporting smoking and vaping cessation. A mixed-methods systematic review was conducted to identify studies published in the last 20 years in five relevant databases. Eight studies, including seven on smoking cessation and one on smoking and vaping cessation, were included. The results showed that, compared to other smoking cessation methods, chatbots can lead to better engagement in treatment, resulting in higher rates of sustained abstinence and improved quality of life. In addition, chatbots can be perceived as empathetic and establish a decent therapeutic alliance thanks to their communication skills. This knowledge could be useful for the development of interactive conversational agents to support smoking and/or vaping cessation. Alternative intervention tools targeting younger generations, such as chatbots, may offer an additional way for public health professionals to reach them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it