Evaluation of Continual Usage of Telemedicine Applications in Indonesia
Bibliographic record
Abstract
Information and communication technologies have great potential to address the problems experienced by developing and developing countries by providing rapid and cost-effective access to quality health care. One of the developments in information and communication technology in the health sector is telemedicine. Telemedicine is the biggest change and challenge that will affect healthcare settings. Advances in telemedicine technology have developed a lot and are very helpful for the progress of public health in general, therefore there are still many challenges and more innovations are needed in the future in advancing telemedicine technology. Some people experience dissatisfaction in queuing for treatment to consult a doctor because it takes quite a long time. The purpose of this study is to determine user satisfaction and whether users will continue to use telemedicine applications. The data analysis used in this study uses the PLS SEM technique using the SmartPLS application. Data samples were taken from users of the Telemedicine application and obtained data collected from April to May 2023 of 140 respondents. There are 12 hypotheses in this study, and it was found that 11 hypotheses were accepted. From the results of the study it has been found that telemedicine application users will continue to use the telemedicine application they use.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".