Planning Locally Relevant Internet Programs for Secondary Prevention of Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Although the Internet has been shown to be an effective tool for supporting behavioural change in other chronic diseases, less in known about the efficacy of, or need for, Internet-based interventions in the prevention of coronary heart disease (CHD). AIMS: We investigated computer literacy, consumer need and perceived usefulness of the Internet as a secondary prevention tool in people with CHD. METHODS: A two-step mixed-method process was used that included a survey and two focus groups. The 12-item survey explored participants' access and confidence using the Internet. For the focus groups, we used standard methodology. RESULTS: We recruited 66 (88% response rate) consecutive cardiac patients; age 36-73 years (mean 64±13), mostly male (85%), whose primary language was predominantly English (67%). Seventy percent had a home computer with Internet access but only 20% reported researching their heart-health online. There was polarity between those with and without Internet access. Further, we found less women than men could complete online forms (p=0.03) and that participants aged over 65 years were less likely to access the Internet (p<0.01) and had lower confidence (p<0.01) than younger counterparts. Focus groups revealed challenges of an online secondary prevention service, but participants valued relevant, practical advice and placed strong emphasis on simple web design. CONCLUSION: Using a mixed-methods process we collected locally sensitive information about Internet usage and recommendations for future online health-management strategies. Some patients have more confidence using the Internet, therefore a range of multi-technological secondary prevention interventions should be considered based on individual need.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".