Increasing Internet use among cardiovascular patients: new opportunities for heart health promotion.
Bibliographic record
Abstract
BACKGROUND: Information technology is revolutionizing health care delivery. Although data exist for other patient populations, awareness and use of information technology in cardiovascular patients have not been well described to date. OBJECTIVES: To assess the awareness and use of information technology in cardiovascular patients over time. METHODS: A survey of consecutive cardiovascular inpatients and outpatients attending a tertiary care, Canadian academic centre was conducted in 2001. Awareness and use of the Internet, use of the Internet for health information, attitudes toward information technology and barriers to use were studied at baseline (n=300) and at one-year follow-up (n=199). The socioeconomic correlation was also examined. RESULTS: Most respondents were aware of the Internet and e-mail. Internet use for health information was prevalent and increased over time (62 of 105 patients [59%] at baseline versus 76 of 105 patients [72%] at one-year follow-up). E-mail use was also prevalent (102 of 189 patients [54%]) but did not increase over time. Cardiovascular patients who used the Internet for health information and e-mail were employed, and were significantly younger and better educated than patients who did not use them; income status was not a significant indicator of Internet or e-mail use. Most patients (95 of 131 patients [72%]) were interested in communicating with their specialists via e-mail. CONCLUSIONS: Information technology is well accepted by cardiovascular patients in Canada. Internet use for health information was prevalent and increased over time. The present findings suggest that the 'digital divide' is evolving, with a narrowing socioeconomic divide, possibly due to the increasing public access to the Internet. This has important implications for patient education and the specialist-patient relationship.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".