The Use of Mobile Health Care Among Medical Professionals in the Sichuan-Chongqing Region: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The emergence and integration of mobile healthcare technology have fundamentally transformed the healthcare industry, providing unprecedented opportunities to improve healthcare services and professional practice. Despite its immense potential, the adoption of mobile healthcare technology among healthcare professionals remains uneven, particularly in developing regions. OBJECTIVE: This study aims to explore the usage and influencing factors of mobile healthcare among healthcare professionals in the Sichuan-Chongqing region of China and make recommendations. METHODS: Convenience sampling was used in a cross-sectional study conducted from November 8th to November 14th, 2023, to survey frontline clinical healthcare professionals at five district-level secondary public hospitals in the Sichuan-Chongqing region. An online questionnaire was used to investigate the usage of mobile healthcare and its influencing factors among the participants. Descriptive analysis and logistic regression analysis were employed in the study. RESULTS: A total of 550 valid questionnaires were completed. Among the surveyed healthcare professionals, only 18.7% used mobile healthcare, with a satisfaction rate of only 50.5%. 81.3% did not use any form of mobile healthcare. The age group of 30-39 was found to be a significant factor influencing the use of mobile healthcare by healthcare professionals (P =.03). The main reasons for not using mobile healthcare among healthcare professionals were: lack of appropriate technical training and support (59.5%), lack of suitable management-specific apps (45.6%), and concerns about increased workload (40.3%). There were significant differences in the single-factor analysis of the reasons for non-use of mobile healthcare among healthcare professionals from different specialties (P=.04). Logistic regression analysis indicated that age was the only significant factor influencing the use of mobile healthcare by healthcare professionals (P =.04). CONCLUSIONS: The utilization rate of mobile healthcare among healthcare professionals in the Sichuan-Chongqing region is low. Age is a significant factor that influences whether healthcare professionals use mobile healthcare. Providing appropriate technical training and support may help improve the enthusiasm of healthcare professionals in using mobile healthcare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".