Socio-economic determinants of public healthcare
Bibliographic record
Abstract
The main socio-economic mission of the healthcare system is to participate in the formation of workforce, strengthen the labor potential of society, and promote economic growth. The national health serves as an important economic resource. The COVID-19 pandemic has proven that losses due to illness and absence of employees from the workplace have negative economic consequences. The bibliometric study conducted via the VOSViewer bibliometric analysis of Scopus publications in 2014-2024 for the query “socio-economic determinants of public healthcare” allowed to structure the scientific work within 8 clusters. The latter showed leading researchers from the USA, the UK, India, Australia, Canada, Germany, Italy and Spain. In 2020-2023, due to the pandemic, publication activity in this area increased significantly. The article conducts a regression analysis of the impact of socio-economic determinants on the level of public healthcare. The public healthcare index is the population life expectancy. Among socio-economic determinants, there are nine components as factor attributes: current healthcare expenditures per capita; income shares of the richest and poorest – 10% and 20%; mortality rates up to 5 years and from suicide; number of people using at least basic sanitation and safe drinking water services, etc. The data for Ukraine from the World Bank statistical base for 2002-2019 were used as an information fundament. The multiple regression generation by the method of stepwise exclusion of variables was implemented using the MS Excel software. The article formulates a list of recommendations for increasing life expectancy. It includes measures to ensure the sanitary and epidemiological well-being of the population, development of healthcare infrastructure. The research also ensures availability of medical services to the population. The study educates and informs the population about the hygiene importance, proper use of sanitation and the consequences of inadequate sanitary conditions for health. Relevance of affordable sanitation improvement programmes for the poor is discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.008 | 0.001 |
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".