Implementasi Kebijakan Badan Penyelenggara Jaminan Sosial (Bpjs) Kesehatan di Kabupaten Bantul Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
The purpose of this study to look at the policy implementation BPJS in Bantul and factors that influence and determine differences in the effect of policy towards participants PBI BPJS Health Insurance and Health Insurance PBI participants not. The method used is a combination of studies (mix method). The study was conducted in Bantul. Unit research conducted at the Office of Health BPJS Branch Yogyakarta, Bantul and Senopati Penembahan Hospital Patient Hospital Penembahan Senopati Bantul. Data collection techniques are observation, interviews, documentation, and questionnaire. Mechanical analysis of this study is to describe the implementation of Board policies BPJS using qualitative descriptive analysis. To explain the factors that affect product moment correlation analysis was used. Furthermore, in order to explain the effect of policy towards participants PBI BPJS Health Insurance and Health Insurance PBI participants not using Analysis of Variance (ANOVA) with the type of single classification analysis of variance (one way ANOVA). The results of this research, policy implementation in Bantul District Health BPJS implemented very well. After doing research results diperolehan communication dimension index values of 4.44 (very good), the dimensions of the resource of 4.59 (very good), the dimensions of the disposition of 4.44 (very good) and the dimensions of bureaucratic structures 4.57 (very good). Variables that affect the implementation of the Implementation of the Social Security Agency (BPJS) Health in Bantul is Context Policy (X2) which is equal to 0839 (very strong). Meanwhile Content Policy variable (X1) significant correlation to variable implementation (Y) is smaller than that of 0768 (very strong). Furthermore, there are differences in the effect of the Health Policy Implementation BPJS PBI participants Health Insurance and Health Insurance PBI participants not at all the dimensions of the dimension of participation with the value Fh = 100, the dimension of service to the value of Fh = 100 and a financial dimension to the value of Fh = 100.
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".