Does It Still Show a Deficit? Arguing Post-COVID-19 Health Financing System in Bogor, Indonesia
Bibliographic record
Abstract
Before the COVID-19 pandemic, the Bogor City Government regulated to cover the health financing claim during the Indonesian National Health Insurance (NHI) integration period due to the lower amount of health care claim per episode in regional hospitals compared to ones that NHI paid. This study aimed to address post-COVID-19 health financing at two hospitals in Bogor City, West Java Province, Indonesia. Descriptive analysis using the aggregate statistical summaries was taken to explore the medical care episodes of the data series at two hospitals for the last two years. Of the 890 checked medical records data, the deficit occurred in 197 (22.1%) medical care episodes, while five (0.6%) exceeded the hospitals' tariffs. The remaining 688 (77.3%) medical care episodes had suits with the Indonesian Case Based Groups. Almost a quarter of medical care episodes in aggregate experienced a deficit in the two years before the pandemic. This study is the first to provide new insight into the discussion on medical care financing in a developing country's post-pandemic era in a newly-implemented NHI system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".