Life Expectancy of Population of the Country: The Role of Health Services Effectiveness
Bibliographic record
Abstract
The impact of health care on life expectancy of population in different countries has been studied. The subject of the study was analysis of efficiency of public health expenditures and their percentage of Gross Domestic Product. The authors employed a relatively new method of comparative analysis, Data Envelopment Analysis (DEA), which involves to measure technical efficiency of activity of an economic entity. DEA method allowed identifying the effectiveness of economic measures in healthcare system, as well as provision of the necessary volume of medical services in different countries. A study was conducted to compare the actual effectiveness of the country's medical services to the maximum possible effectiveness. As factor indicators, the summary of health expenditure and its percentage of GDP were considered. The average life expectancy of countries was taken as an average resulting indicator. According to the results, all the surveyed indicators proved to be the most effective measures in providing population with medical services in such countries as Andorra and San Marino, Monaco. It was determined that in order to increase the average life expectancy for one year, an average annual increase in the expenditure on the health care system by 0.48% of GDP is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".