Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study
Bibliographic record
Abstract
BACKGROUND: Although mobile health (mHealth) has the potential to transform health care by delivering better outcomes at a much lower cost than traditional health care services, little is known about mHealth adoption by hospitals. OBJECTIVE: This study aims to explore the determinants of mHealth adoption by hospitals using the technology-organization-environment (TOE) framework. METHODS: We conducted an interviewer-administered survey with 87 managers in Chinese public hospitals and analyzed the data using logistic regression. RESULTS: The results of our survey indicate that perceived ease of use (β=.692; P<.002), system security (β=.473; P<.05), top management support (β=1.466; P<.002), hospital size (β=1.069; P<.004), and external pressure (β=.703; P<.005) are significantly related to hospitals' adoption of mHealth. However, information technology infrastructure (β=.574; P<.02), system reliability (β=-1.291; P<.01), and government policy (β=2.010; P<.04) are significant but negatively related to hospitals' adoption of mHealth. CONCLUSIONS: We found that TOE model works in the context of mHealth adoption by hospitals. In addition to technological predictors, organizational and environmental predictors are critical for explaining mHealth adoption by Chinese hospitals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".