Think-aloud test of online education for patients with cardiac diseases: do they meet the needs of women?
Bibliographic record
Abstract
Patient education is a core component of cardiovascular rehabilitation (CR). Recent guidelines call for tailored materials to support women, given their unique needs and preferences. This study investigated women patient's perspectives of online CR educational materials (Cardiac College, Toronto). Through a cross-sectional design, a think-aloud protocol followed by semistructured qualitative interviews was used to collect data on women's perceptions of the comprehensive educational webpages with regard to content, format, visuals, length/volume, difficulty, applicability, implementability and suggestions for improvement. Thirty-eight current and previous CR program participants completed 1-h Zoom interviews. Transcripts were coded thematically using best practices in NVivo by two researchers independently. Four themes emerged: (I) materials met their needs (content, presentation, and empowerment), (II) suggestions for improvement in presentation (website design, text, and visuals), (III) content (volume, additions, and difficulty), and (IV) optimizing reach and implementability (inclusiveness, barriers, and dissemination). Overall, the education content met women's needs and was relatable, but should be updated for currency, visual appeal, and searchability. Cardiac College for Women may meet these needs and preferences.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".