Analysis of the Influence of Physical Facility, Executive Staff, Information and Finance on the Satisfaction of Outpatient Health bpjs Participants at rsu Prima Husada Sidoarjo
Bibliographic record
Abstract
Social security is a type of service provided by the government to the community based on the state's ability and capability to give assistance and convenience to the community. Since January 1, 2014, the Ministry of Health of the Republic of Indonesia has established Badan Penyelenggara Jaminan Sosia (BPJS) to optimise the JKN programme. BPJS is a legal company founded to organise health insurance. There are numerous programmes under the guarantee programme, including health insurance programmes, accident insurance programmes, old age insurance programmes, and death insurance programmes. The aim of this study is to examine at the influence of physical facilities, executive staff, information, and finances on outpatient BPJS Health patient satisfaction at Jagir Surabaya Health Centre. This study takes a quantitative method. This study's strategy included multiple linear regression. The individuals investigated were 90 patients recruited from the Prima Husada Sidoarjo Hospital using a proportionate random sample technique. According to the study's findings, 47.7% of physical facilities, 13.4% of implementing staff, 25.2% of information, and 24.9% of money at Prima Husada Hospital had an impact on BPJS Health patient satisfaction. The hospital's function is expected to try to improve management, particularly health management, in order to manage better.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".