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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".