ANALISIS KEBIJAKAN SURAT PERNYATAAN MISKIN PADAPROGRAM JAMINAN KESEHATAN DAERAHDI KABUPATEN JEMBER
Bibliographic record
Abstract
Background: Utilization of funds Regional Health Insurance Program (Jamkesda) in the first quarter of 2012 reached 57% of the total budget provided for a year, where most of the people who need health care use Poor Statement Letter (SPM). Utilization of SPM in first quarter of 2012 has increased higher than the previous year, it is this which encourages researchers to do research. Objective: This study aimed to identify the cause of the increased use of SPM in the Regional Health Insurance Program in Jember by conducting policy analysis Poor Statement Letter (SPM) on the Regional Health Insurance Program (Jamkesda) in Jember District. Methods: The study was a descriptive research design of a case study involving two data sources, primary and secondary data. The primary data obtained through interviews with respondents and secondary data obtained through from report of the Regional Health Insurance Program in Jember District. Results: There were several causes of the high use of SPM in Jamkesda Program in Jember during the year 2012 one of them is because there are many people who are below the poverty line are not covered Jamkesmas and not have a Jamkesda card, this is due to poor data collection system is not running better. The results also showed that the accuracy of the SPM user in Jember is 91%. Conclusion: There are many issues associated with the Jamkesda card be one cause of the high use of SPM in Jamkesda Program in Jember during the year 2012. Utilization of SPM in Jember really actually used by the poor so right on target.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".