Examining the factors affecting the adoption of e-health innovative technology
Bibliographic record
Abstract
In today's world, many modern health facilities have started using e-health with the aim of improving health services by managing its costs, patient waiting time, and other services. Nevertheless, there are numerous studies exploring the barriers to e-health adoption. Concentrating on innovation in the healthcare industry, the present study explores the external factors that predict patients' behavioural intention to use a personal health record (PHR) as an important part of the electronic patient-physician relationship. Empirical research is used to identify a conceptual framework illustrating the relation between patients' behavioural intention and the proposed factors: governmental incentives, physician support and hospital management support. The framework is tested by using data collected from Canada as a case study through a well-designed survey. The results of multiple regression analysis indicate that the proposed factors were significantly predicted as the perceived ease of use and perceived usefulness of PHR innovative technology. The perceived usefulness factor was significantly predicted in the behavioural intention to use PHR. Some procedures and actions should be considered by government and healthcare policy makers to manage the adoption and support the usage of PHR application.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".