Comparative study of care expenditures for private and public health
Bibliographic record
Abstract
Introduction: Health is one of the most important factors of human capital. Health affects labor supply both qualitatively (higher productivity) and quantitatively (not missing working days due to illness). Most countries have experienced a rapid growth in their health care costs in the past years. But nfortunately, some countries still think that any activity in the field of improving health will increase costs It should be noted that the skilled workforce can be the focus of development and increase production when it has physical health and a cheerful spirit. Although health expenses and then human capital play a significant role in the development of societies, however, the factors affecting health expenses and the impact of each of its constituent factors have not been well investigated and analyzed.Therefore, according to the increasing growth of the expenses of this sector, it seems important to identify the components that affect the health expenses.Therefore, in order to properly plan in the health-economic fields, one should have a precise and accurate understanding of the factors affecting health expenditures.The same subject led the current research to investigate the comparative-comparative factors affecting private and public health care expenditures in selected developed and developing countries.Methodology: This research used time series data for the period 2000-2019 and based on the panel data method, the variables affecting health expenditure for the private and public sectors of 15 selected developed countries including: Switzerland, Australia , Canada, Netherlands, Singapore, Germany, Sweden, Italy, USA, Norway, France, Japan, Denmark, Austria and Belgium; And also 15 selected developing countries including: Islamic Republic of Iran, Turkey, Georgia, Azerbaijan, China, Serbia, Ukraine, Peru, Lebanon, Panama, Albania, Armenia, Cuba, Mexico and Costa Rica will be analyzed and investigated. Variables of gross domestic product (GDP), life expectancy (EXP), population over 65 years old (POP), population under 14 years old (age), urbanization rate (URB), literacy rate (EDU), out-of-pocket payments (OOP) And foreign aid (ODA) is one of the influencing factors on health care expenses in this research. Also, the statistics and figures used in this research were extracted from the World Bank and the World Atlas (Knoema).Results and Discussion: The variable coefficient of GDP, which is one of the effective variables in private and public health care expenditures in developed and developing countries, is positive and significant; the estimated coefficient of life expectancy variable is positive and significant.The impact of the variable population over 65 years old in developed and developing countries in both private and public sectors has been positive and significant.Also, the population under 14 years of age in both groups of developed and developing countries had a negative and significant relationship with health care expenditures in both private and public sectors; Also, the variable impact of urbanization rate in developed and developing countries has been positive and significant in both private and public sectors;The effect of the out-of-pocket variable has been negative and significant in developed countries and positive and significant in developing countries .And the variable coefficient of foreign aid in developing countries is negative and significant. And finally, the results of this study showed that there is no significant statistical relationship between the literacy rate and health costs.Conclusion: Considering the share of public and private sectors in health expenditures in developing countries, governments, as the largest public institution, should take the necessary measures to increase the share of public sector in health expenditures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".