Sağlık Ekonomisi Perspektifinde Seçilmiş Ülkelerin Sağlık Göstergelerinin Değerlendirilmesi
Bibliographic record
Abstract
Health economics is a science that examines the health structures of countries in economic terms. Health indicators, also, reveal both microeconomic and macroeconomic conditions of the health economy. The good health status of a country means gains in both social and economic terms for that country. On the other hand, economic development is reflected in health indicators. Important health indicators of G8 countries and developing countries Turkey, China and India are appear in the studuy. In this context, it is aimed to compare developing countries both among themselves and with developed countries. As a result of the evaluations, it has been observed that although Russia's health infrastructure is relatively good, it is close to developing countries in other health indicators. While it is seen that Japan has the best values in terms of health among all countries, the USA has become prominent in health expenditures. Turkey has progressed in the field of health, but this situation is progressing relatively slowly. Italy, Germany, France, Canada and England are similar in health status. China, on the other hand, is advancing rapidly in health indicators. The role of the public in health expenditures is relatively high in Turkey and Germany. It has been found that most developed countries exhibit more positive results in health indicators than developing countries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".