Strengthening the Health System to Address the COVID-19 Surge: An Empirical Study in South Kalimantan Province, Indonesia
Bibliographic record
Abstract
The COVID-19 case entered South Kalimantan Province on May 12, 2020, and spread throughout all districts/cities. This research aims to examine the ability of the South Kalimantan Provincial Health System to address the COVID-19 pandemic. This study analyses secondary data from the South Kalimantan Provincial Health Office and in-depth interviews with policymakers. This study assesses the capacity of the South Kalimantan health system in managing the COVID-19 pandemic. Findings reveal significant challenges, including hospital bed shortages, high infection rates among health workers (10.02%), and limited ventilator availability. Despite allocating 23.27% of the health budget to the pandemic response, key subsystems such as human resources, drug supply, and coordination mechanisms remained under strain. Strengthening these subsystems is essential for better preparedness in future health emergencies. In conclusion, strengthening the health system is very important in overcoming the COVID-19 pandemic, and it is hoped that the lessons of the COVID-19 pandemic will make the health system more prepared to address the disease pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".