Factors Influencing Adoption and Use of Telemedicine Services in Rural Areas of China: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The shortage of medical resources in rural China reflects the health inequity in resource-limited settings, whereas telemedicine could provide opportunities to fill this gap. However, evidence of patient acceptance of telemedicine services from low- and middle-income countries is still lacking. OBJECTIVE: We aimed to understand the profile of patient end-user telemedicine use and identify factors influencing telemedicine service use in rural China. METHODS: Our study followed a mixed methods approach, with a quantitative cross-sectional survey followed by in-depth semistructured interviews to describe telemedicine use and its associated factors among rural residents in Guangdong Province, China. In the quantitative analysis, explanatory variables included environmental and context factors, household-level factors, individual sociodemographic factors, access to digital health care, and health needs and demand factors. We conducted univariate and multivariate analyses using Firth logistic regression to examine the correlations of telemedicine uptake. A thematic approach was used, guided by the Social Cognitive Theory for the qualitative analysis. RESULTS: A total of 2101 households were recruited for the quantitative survey. With a mean age of 61.4 (SD 14.41) years, >70% (1364/2101, 72.94%) of the household respondents were male. Less than 1% (14/2101, 0.67%) of the respondents reported experience of using telemedicine. The quantitative results supported that villagers living with family members who had a fever in the past 2 weeks (adjusted odds ratio 6.96, 95% CI 2.20-21.98; P=.001) or having smartphones or computers (adjusted odds ratio 3.71, 95% CI 0.64-21.32; P=.14) had marginally higher telemedicine uptake, whereas the qualitative results endorse these findings. The results of qualitative interviews (n=27) also supplemented the potential barriers to telemedicine use from the lack of knowledge, trust, demand, low self-efficacy, and sufficient physical and social support. CONCLUSIONS: This study found extremely low use of telemedicine in rural China and identified potential factors affecting telemedicine uptake. The main barriers to telemedicine adoption among rural residents were found, including lack of knowledge, trust, demand as well as low self-efficacy, and insufficient physical and social support. Our study also suggests strategies to facilitate telemedicine engagement in low-resource settings: improving digital literacy and self-efficacy, building trust, and strengthening telemedicine infrastructure support.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".