Usability and Feasibility Testing of an Atrial Fibrillation Educational Website with Patients Referred to an Atrial Fibrillation Specialty Clinic
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to design, usability test, and explore the feasibility of a web-based educational platform/intervention for patients with atrial fibrillation (AF) as part of their virtual AF care. METHODS: Participants were patients attending a specialized AF clinic. The multiple mixed-methods design included website design, think-aloud usability test, 1-month unstructured pre-testing analysis using Google Analytics, follow-up interviews, and a non-randomized one-group feasibility test using pre/post online surveys and Google Analytics. RESULTS: = 9) website activity averaged four sessions (SD = 2.6) at 10 (SD 8) minutes per session during a 1-month study period. In the feasibility test, 30 patients referred to AF specialty clinic care completed the baseline survey, and 20 of these completed the 6-month follow-up survey. A total of 19 patients accessed the website over the 6 months, and all 30 participants were sent email prompts containing information from the website. Health-related quality of life, treatment satisfaction, household activity, and AF knowledge scores were higher at follow-up than baseline. There was an overall downward trend in self-reported healthcare utilization at follow-up. CONCLUSIONS: Access to a credible education website for patients with AF has great potential to complement virtual and hybrid models of care.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".