How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective
Bibliographic record
Abstract
BACKGROUND: The last decade has witnessed many achievements in China's health care industry, but the industry still faces major challenges among which the uneven distribution of medical resources and the imbalance between supply and demand are the most pressing problems. Although mobile health (mHealth) services play a significant role in mitigating problems associated with health care delivery, their adoption rates have been low. OBJECTIVE: The objective of this study was to explore the impact of resource scarcity and resource accessibility on the adoption of mHealth from the perspective of resource competition, to examine the concerning factors, and to provide a theoretical basis for promoting mHealth in China. METHODS: We used 229,516 original registration records of outpatients to conduct an empirical analysis to examine the adoption of mHealth services from the perspective of resource competition. RESULTS: The adoption rate of mobile services for outpatients was low, accounting for only 31.5% (N=71,707). The empirical results indicated that resource scarcity (beta=.435, P=.01) and accessibility (beta=-.134, P=.02) have a significant impact on the adoption of mHealth. In addition, gender (beta=.073, P=.01) and age (beta=-.009, P<.001) are significantly related to adoption of mHealth. Experience with mHealth has a moderating role in the relationship between resource scarcity (beta=-.129, P=.02), accessibility (beta=.138, P=.04), and adoption of mHealth. CONCLUSIONS: In this study we demonstrate that the external environment (resource scarcity and resource accessibility) has a significant impact on the adoption of mHealth. This study also demonstrates that experience with mHealth has a moderating role in the relationship between the elements of the external environment. Finally, we confirm that mHealth is a key factor in the delivery and allocation of medical resources and provide a theoretical basis for government agencies to develop policies on mHealth.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".