Clinical Perspectives on the Development of a Gamified Heart Failure Patient Education Web Site
Bibliographic record
Abstract
Heart failure is a complex, chronic disease that requires self-care to manage, and patients need support and education to perform adequate self-care. Although electronic health interventions to support behavior change and self-care in cardiovascular disease are gaining traction, there is little engaging online education specifically designed for heart failure patients. This paper describes the design and development of a heart failure self-care patient education Web site that integrated gamification, meaning the use of game design elements in a non-game context. We sought feedback on the Web site from a group of heart failure clinicians in a focus group using a semi-structured interview guide, and data were analyzed thematically. Clinician input during the design phase touched on themes such as patients' decision-making in heart failure and older adults' adoption of technology. Clinicians recommended that a narrative gamification technique should reflect real-life dilemmas patients encounter in their self-care. Clinicians also discussed the need to carefully plan reward-based gamification techniques to avoid unintended effects. Overall, a gamified Web site has the potential to support heart failure self-care, but efforts are needed to address the disparity of those with limited computer literacy or access.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".