Analisis Efektivitas E-Health Menggunakan Metode E-Govqual dan IPA Di Puskesmas Gading Kota Surabaya
Bibliographic record
Abstract
E-Health, a public service that leverages Information Technology to enhance the health sector's knowledge base, faces developmental challenges. At Gading Health Center in Surabaya City, dissatisfied assessments of the health center's rating stem from discontent with both services and the e-Health system. Issues include disparities in service time compared to the targeted e-Health Service System queue, as well as variations between online registration times and the actual services provided at the health center. Moreover, human resource factors such as knowledge, experience, and technological skills among Gading Community Health Center staff significantly influence the successful implementation of the e-Health system. This study employs e-GovQual and IPA methods to evaluate the effectiveness of e-Government Implementation, focusing on dimensions like efficiency, trust, reliability, and citizen support. The research findings, analyzing the effectiveness of e-Health at Puskesmas Gading using e-GovQual and IPA methods, lead to the conclusion that the implementation of e-Health at Puskesmas Gading in Surabaya City positively impacts the quality of health services
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".