Urban-rural difference in patients utilizing the service of telehealthcare
Bibliographic record
Abstract
There are concerns about the unbalanced distribution of healthcare resources between rural and urban areas. There have been attempts to use existing healthcare resources more effectively through telehealthcare. Usability is an important indicator for evaluating patient behavior and determining service improvements. Nevertheless, usability has not been studied extensively enough. This study analyzed the usability differences between urban and rural areas in Taiwan for a web portal used in a telehealthcare program. Data were collected for two years. Usability data includes the frequency of web portal patient logins, the frequency of glucose measurements, whether the records were transmitted to the system through 3G networks automatically or were manually inputted, and the correlation of the mean 3-month daily glucose levels and HbA1c results. Patients in urban areas logged into the web portal more frequently (p < .001) and recorded glucose levels more frequently (p = .003). More patients in the rural area transmitted their daily glucose levels using devices (p < .001). Mean 3-month daily glucose levels and HbA1c results appear to be highly consistent. Patients in urban areas did not readily change glucometer habits but were willing to log in to web portal and record daily glucose levels manually. Patients in rural areas were willing to use the 3G glucometer to transmit data more frequently. For patients in urban areas, web portals should provide more information and smart applications. For patients in rural areas, the application should be simple and easy to use.
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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 | 0.001 | 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 | 0.001 |
| 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".