POLA PENGELUARAN RUMAH-TANGGA UNTUK KESEHATAN PADA KELOMPOK MARJINAL DAN RENTAN
Bibliographic record
Abstract
Utilization of health care is infl uenced by many factors. Most important are geography, socioeconomic, gender inequality, culture, and quality of care. This study aimed at providing policy formulations evidence based in formation for RRO poor, The study is a cross sectional study using National Socioeconomic Survey data set of 1998 representing about 205.000 households. This analysis is conducted to respond the equity issue in Indonesia, with particular emphasize to equity of access (health services use). The study revealed that in urban areas 88.8% of the people pay the outpatient services from their out-of-pocket, while in rural the figure is 94.3%. The data shows that in urban areas, among the lowest group, expenditure for health placed about 13% of non-food expenditure. In rural areas the health expenditure accounted to around an average of 12% non-food expenditure. For the highest group of socioeconomic status, expenses on health reached only 10% of non-food expenditure. In rural areas, the highest group has spent for health about 14% of their non-food expenses. Most of the poor (almost 90%) have spent for health below a quarter of non-food expenses. In general, households have spent about 6-15% and 20-71% of their non-food expenses for outpatient and in-patient respectively. Those who spent more than 50% of their non-food expenditure for outpatient is accounted to 3.63% of the households in urban and 4.31% in rural areas. A relatively small percentage of the households in urban and rural areas used a catastrophic spending for outpatient care. Nevertheless, almost 77% of them in urban and rural areas have spent more than 50% of their non-food expenditures per month for inpatient care. This catastrophic spending has affected 72.88% of the households in the urban area and 80.98% in rural areas. Apparently the financial risk is very high for the people in responding the probability of loss due to sickness. Since most Indonesian people are not insured, this phenomenon will become a burden for them.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".