Risk Adjustment of Capitation Payment System: What Can Indonesia Adopt from other Countries?
Bibliographic record
Abstract
Capitation calculation in Indonesia is not adjusted by individual and aggregate risk. Without risk adjustment, capitation rates are likely to overpay or underpay primary care. This study aimed to review risk-adjusted capitation payment in other countries for evaluation of capitation payment system in Indonesia. The conduct and reporting of this systematic review followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study used comprehensive electronic search in five databases: Pubmed, Proquest, Scopus, PMC, and EBSCOHost. Search results from five databases in April 2018, yielded a total 19 titles that will continue to review the full article and at the end, 4 articles included for systematic review. Based on risk adjustment of capitation payment system in UK, USA, Canada and Sweden, Indonesia may initiate the use of risk adjustment based on the distribution of age and sex. Then Indonesia can develop risk adjustment based on diagnosis and socioeconomic factors to create more fair and accurate capitation rates for primary care.
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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 | 0.047 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".