Treatment Interest of Badan Penyelenggara Jaminan Sosial (Social Security Organizing Agency) Patients in Balowerti Public Health Center Kediri City
Bibliographic record
Abstract
The Social Security Organizing Agency (BPJS) which was established in 2014, implements the National Health Insurance Program (JKN). While JKN positively affects national health and increases the financial flow of private hospitals, there is a significant financial deficit, which can be covered by the involvement of informal private-sector workers, whose loyalty to the hospital is mainly influenced by hospital’s environment, communication with staff, and service quality. Previous studies indicate that in Indonesia loyalty to the public hospitals can have no relationships with service quality, to test this assumption, a sample of 126 subjects was recruited at the Balowerti City Health Center, Kediri City. All participants of the study received premium assistance beneficiaries (PBI) of BPJS insurance, which is fully subsidized by the government. Despite this, the main part of the sample evaluated their perception of the Balowerti City Health Center and the quality of its service as average or poor. Ordinal regression confirmed the existence of the influence of service quality and perception of the hospital on the behavioral intention of patients. Refers to perception of low service quality is the main reason for insufficient involvement if JKN. According to the previous studies, a lack of time for communication with the patient, long time of waiting, and a lack of information of BPJS are main reasons of patient dissatisfaction and low enrollment in JKN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".