Regulation of Law of the Republic of Indonesia Number 17 of 2023 on Control of Catastropic Diseases Linked to Financing of National Health Insurance Based on Dignified Justice
Bibliographic record
Abstract
This study aims to analyze the provisions in Law of the Republic of Indonesia Number 17 of 2023 concerning Catastrophic Disease Control, especially related to the financing of the National Health Insurance (JKN) based on the principle of dignified justice. This study uses a normative legal method with a legislative and comparative approach, as well as descriptive-analytical specifications. The analysis was carried out by examining related regulations, government responsibilities, and comparative studies with other countries such as the United States, England, Canada, and Australia. The results of the study indicate several challenges in the implementation of catastrophic disease control in Indonesia, especially in financing through JKN. Although Law Number 17 of 2023 has provided a comprehensive legal framework, there are gaps in the efficient use of JKN funds and disparities in access to health services between urban and rural areas. In addition, this study also found that the role of the government as a regulator and service provider creates potential conflicts of interest. Based on the legal analysis conducted, policy recommendations are proposed to improve the regulation and implementation of catastrophic disease control through improving institutional structures, increasing financing transparency, and strengthening the role of government in ensuring fair and quality access.
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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 | 0.004 | 0.001 |
| 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".