Primary care physician payment mechanisms toward universal health coverage: A study of Iran and selected countries
Bibliographic record
Abstract
BACKGROUND AND AIM: Primary care physician (PCP) payment mechanisms can be important tools for addressing issues of access, quality, and equity in health care. The purpose of the present study is to compare the PCP payment mechanisms of Iran, Canada, Australia, New Zealand, England, Sweden, Norway, Denmark, the Netherlands, Turkey, and Thailand. METHODS: This is a descriptive-comparative study comparing the PCP payment mechanisms of Iran and selected countries in 2020. Data for each country are collected from reliable databases and are tabulated to compare their payment models. Framework analysis is used for data analysis. RESULTS: The results are provided in terms of PCP payment mechanisms, adjusting factor for capitation, reasons for fee-for-service payment, the role of pay-for-performance (PFP) programme, domain and indicators, and reasons for developing PFP in each country. CONCLUSION: The majority of the countries with high UHC service coverage index have applied a mix of PCP payment mechanisms, most of which include capitation and PFP. Moreover, adjusting capitation by factors such as age, sex, and health status will lead to provision of better services to high-risk populations. In recent years, PFP has been paid to Iranian PCPs in addition to salary. Given the various existing models for primary health care in Iran and the increasing burden of chronic diseases, a more appropriate combination of payment mechanisms that create more incentives to provide active and high-quality care should be developed. Also, when developing payment mechanisms, the required infrastructure such as electronic health record should be considered.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".