Using a healthbot to improve varenicline adherence: Insights from healthcare providers
Bibliographic record
Abstract
Varenicline, a highly efficacious smoking cessation medication, has high non-adherence rates. Healthbots, apps that use artificial intelligence to converse with users and help manage health by tracking data, providing resources, and sending notifications, can provide tailored medication support and may improve medication adherence. This study aimed to understand, from the perspectives of healthcare providers, how a healthbot can improve varenicline adherence and what would influence them to recommend it.. We interviewed 19 providers across Ontario and analyzed qualitative data using the theoretical domains framework, a guide made of categories influencing behaviour change. A common facilitator for adherence was effective communication. A common barrier to adherence was side effects, which providers believed a healthbot could address by providing mitigation strategies. Providers mentioned they would not recommend a healthbot to patients with poor technological literacy but would if they receive positive feedback from others. This information will help develop and implement a healthbot for varenicline adherence.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".