Formative Provider Testing of a New Encounter Decision Aid for Smoking Cessation: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Smoking cessation is an essential part of preventing and reducing the risk of smoking-associated morbidity and mortality. However, there is often little time to discuss smoking cessation in primary care. Decision aids (DAs) designed for clinic visits (encounter DAs) need to be clear, short, and concise to optimize therapeutic education, increase interaction, and improve the therapeutic alliance. Such a DA for smoking cessation could potentially improve counseling and increase the use of pharmacological treatments. OBJECTIVE: We aimed to collect feedback on an electronic encounter DA that facilitates physician-patient interaction and shared decision-making for smoking cessation in primary care. METHODS: We developed an electronic, encounter DA (howtoquit.ch) from a paper version created by our team in 2017 following user-centered design principles. The DA is a 1-page interactive website presenting and comparing medications for tobacco cessation and electronic cigarettes. Each smoking cessation medication has a drop down menu that presents additional information, a video demonstration, and prescribing information for physicians. To test the DA, we submitted a questionnaire to approximately 20 general practitioner residents of an academic general medicine department, 5 general practitioners, and 6 experts in the field of smoking cessation. The questionnaire consisted of 4 multiple-choice and 2 free-text questions assessing the usability or acceptability of the DA, the acquisition of new knowledge for practitioners, the perceived utility in supporting shared decision-making, perceived strengths and weaknesses, and whether the participants would recommend the tool to other clinicians. RESULTS: In all, 6 residents, 3 general practitioners in private practice, and 2 tobacco cessation experts completed the questionnaire (N=11), with 4 additional experts providing open-text feedback. On the 11 questionnaires, the DA was rated as practical and intuitive (mean 4.6/5), and providers felt it supported shared decision-making (mean 4.4/5), as comparisons were readily possible. Inclusion of explanatory videos was seen as a bonus. Several changes were suggested, like grouping together similar medications and adding a landing page to briefly explain the site. Changes were implemented according to end-user comments. CONCLUSIONS: The overall assessment of the encounter DA by a group of physicians and experts was positive. The ultimate objective is to have the tool deployed and easily accessible for all to 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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".