Abstract 4362666: Co-Design and Development of a Web-Based Decision Aid to Support Team-Based Shared Decision-Making to Advance Cardiovascular Health: A Mixed Method Study
Bibliographic record
Abstract
Introduction: Team-based care and shared decision-making (SDM) have been recommended to improve the management of cardiovascular disease (CVD) risk factors. However, decision aids to support team-based SDM for health behavior change and medication management in adults with multiple CVD risk factors are lacking. Objectives: To design and develop a web-based decision aid in health behavior change and medication management to improve multiple CVD risk factor management. Methods: A mixed method study was conducted to develop a web-based decision aid in health behavior change and medication management to improve CVD risk factor management using human-centered design principles and the International Patient Decision Aid Standards. The development process included three phases: (1) SDM workflow and needs assessment: we conducted a modified two-round Delphi study with 18 expert panelists including cardiologist, nurse, community health worker, pharmacist, physician assistant to develop the SDM workflow in multiple CVD risk factor management, (2) Needs assessment: we interviewed 6 clinicians and conducted 3 focus groups with 13 adult patients living with multiple CVD risk factors to understand clinician and patient perceived barriers and facilitators to SDM for CVD risk factor management; and (3) Prototype design and development: we conducted a co-design workshop to design and develop the Preferred-Heart prototype with 15 key stakeholders including software developer, patients with CVD risk factors, primary care providers, pharmacists, and community health workers. Results: The Delphi process resulted in a SDM workflow to improve health behavior change and medication management with 75 items and 8 SDM steps (Figure 1). The qualitative study identified multi-level barriers and facilitators to SDM in CVD risk factor management (Figure 2). Figure 3 shows the developed prototype of the web-based decision aid for multiple CVD risk factor management (“Preferred-Heart”). Conclusion: Key stakeholder input informed development of a web-based decision aid prototype to support health behavior change and medication management to improve multiple CVD risk factor management. Future studies are needed to test the usability, acceptability, and effectiveness of the developed decision aid in improving SDM and health outcomes.
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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 | 0.047 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".