Analysis Socioeconomic Influence on the Utilization of Health Service in the Inpatient Room of Faisal Islamic Hospital Makassar
Bibliographic record
Abstract
Hospitals in carrying out their role cannot be separated from problems, both from within and from outside that can interfere with the mechanism of work of the hospital in providing health services for the community. Faisal Islamic Hospital Makassar is one of the private hospitals in the city of Makassar which in the last 3 years has decreased the value of BOR. The purpose of this study was to find out the socio-economic influence on the utilization of health services in the inpatient room of Faisal Islamic Hospital Makassar. This research is a cross-sectional study. A total of 99 people were sampled in the inpatient room of Faisal Islamic Hospital Makassar by taking a sample using the purposive sampling method. Data is collected by conducting interviews with respondents. Data processing is done by frequency distribution analysis and univariate and multivariate analysis with the Binary Regression approach namely logit and probit analysis. Based on the results of the study, it was obtained that socioeconomic variables that have an influence on the utilization of health services in the Faisal Islamic Hospital Makassar is the ownership of health insurance with the results of p-value< 0.1 is logit 0.299 and probit of 0.000. For the government to evaluate the ownership of health insurance, because there are still respondents who do not have health insurance where this is not in accordance with the objectives of the National Health Insurance program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".