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 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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".