Diagnosing The Dynamic Drivers of Healthcare Expenditure in Organisation of Islamic Cooperation (OIC) Countries
Bibliographic record
Abstract
The foremost impact of healthcare system is for the individuals to have the right and privileges to access enhanced healthcare services. The demand for health, innovation and sustainable healthcare systems has also been gaining better prominence and consideration in numerous countries, and it is perceived as one of the major contributors to the economic growth and development. This paper looks at the dynamic drivers of healthcare expenditure in Organisation of Islamic Cooperation (OIC) countries from 1990 to 2015. The dynamic panel system Generalized Method of Moments (GMM) technique was used for the study analysis. The findings show that the income, life expectancy, share of population between the age group of 65 years and above, share of population age under 15 years, out-of-pocket payment, research and development (technology) in healthcare and consumer price index were the drivers of healthcare expenditure in OIC countries. In view of this, the study differs from recent and previous studies, because the study offers novel empirical findings as the income per capita is above one, which is about 1.90 and inelastic. This proves that healthcare in OIC countries is a luxury goods.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".