Factors Affecting the Adoption of Connected Objects in e-Health: A Mixed Methods Approach
Why this work is in the frame
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Bibliographic record
Abstract
The development of connected objects (COs) offers a new perspective on both e-health and the economy; however, the factors leading to the adoption of e-health and COs remain somewhat misunderstood. Using a sequential combination of qualitative and quantitative research methods, this study investigates the factors affecting the adoption of COs in e-health. After conducting semi-structured interviews, a research model was developed and tested on a sample of 226 professionals in an online survey. The findings of this mixed methods study indicate that perceived convenience and social influence mainly affect adoption. Five other factors were also found to contribute to CO adoption: compatibility, object interoperability, object integration, result demonstrability and reputation. This study contributes to the understanding of CO adoption in e-health and provides useful insight into how to successfully launch connected devices.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it