Attitudes towards acceptance of an innovative home-based and remote sensing rehabilitation protocol among cardiovascular patients in Shantou, China.
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation (CR) protocols have diversified to include home-based cardiac tele-rehabilitation (HBCTR) as an alternative to hospital-based or center-based CR. To adopt the use of home-based cardiac tele-rehabilitation, it is necessary to assess cardiac patients' attitudes towards acceptance of such e-health technology, especially in China where knowledge of such technology is deficient. METHODS: Interviews were conducted in the First Affiliated Hospital of Shantou University Medical College, Shantou, China. After percutaneous coronary interventional (PCI) surgery, patients completed the survey. RESULTS: Among the 150 patients, only 13% had ever heard of HBCTR. After an introduction of our HBCTR program, 60% of patients were willing to participate in the program. From our multivariate analysis of questionnaire data, age (OR: 0.92, 95% CI: 0.86-0.98; P = 0.007), average family monthly income (OR: 0.13, 95% CI: 0.05-0.34; P < 0.001), education level (OR: 0.24, 95% CI: 0.10-0.59; P = 0.002) and physical exercise time (OR: 0.19, 95% CI: 0.06-0.56; P = 0.003) were independent predictors for acceptance of HBCTR. From the reasons for participation, patients selected: enhanced safety and independence (28.3%), ability to self-monitor physical conditions daily (25.4%), and having automatic and emergency alert (23.1%). Reasons for refusal were: too cumbersome operation (34.3%) and unnecessary protocol (19.4%). CONCLUSIONS: Most patients lacked knowledge about HBCTR but volunteered to participate after they have learned about the program. Several personal and life-style factors influenced their acceptance of the program. These indicate that both improvement of technology and better understanding of the program will enhance active participation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".