Assessing Internet Quality Across Public Health Centers in Indonesia: Cross-Sectional Evaluation Study
Bibliographic record
Abstract
Background: Primary health care centers (Puskesmas) serve as the cornerstone of Indonesia's health care system, providing integrated services aimed at improving individual health through prevention, treatment, and health promotion. To fulfill these roles effectively, robust technological infrastructure, particularly reliable internet connectivity, is increasingly essential. Assessing the availability and quality of internet access in Puskesmas is therefore a critical step in understanding their readiness to implement digital health initiatives and fulfill their responsibilities in delivering accessible and effective healthcare services. Objective: This study provides a national baseline assessment of internet quality and its relevant IT infrastructure in more than 10,000 Puskesmas across Indonesia. Methods: A cross-sectional survey was conducted throughout all Puskesmas (10,382) in 34 provinces in Indonesia, using an online questionnaire. Categorization was done to analyze internet quality level results. Results: A total of 10,378 out of 10,382 public health centers (99.96%) participated in this study. Overall, 745 of 10,382 (7.18%) did not have internet access, 1487 (14.33%) had limited internet access, 5567 (53.64%) had sufficient internet access, and 2579 (24.85%) had sufficient and fast internet access. Moreover, 832 of 10,382 Puskesmas (8.02%) did not have 24-hour electricity, 44,196 (43.7%) had a central processing unit (CPU) with i3 specifications, 43,044 (42.56%) had 512 GB hard disk capacity, and 67,272 (66.5%) used antivirus. Conclusions: Although 79% (8201/10,382) of Puskesmas in Indonesia already had sufficient internet access, 21% (2180/10,382) still have limited and insufficient access. To ensure universal internet availability, it is essential to build collaborative support among internet providers and government to foster the availability and use of internet satellites, high-quality computers, and electrical power to support internet connectivity.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".