The Correlation between Economic Convergence and Health Indices in Developed Countries
Bibliographic record
Abstract
Background: Economic convergence signifies diminishing income disparities among global or regional economies and their eventual disappearance. It is also linked to economic growth and key health indicators. We aimed to assess the association between economic convergence and key health indicators in developed countries called G7 (USA, UK, Germany, France, Italy, Japan, and Canada). Methods: We examined G7 health and economic indicators from 2000 to 2021 using panel data analysis. We compared balanced and unbalanced panel datasets to address missing data and applied suitable methods to handle missing health indicators. Results: Little's MCAR test confirmed random missing data in the unbalanced panel, enabling us to impute missing values as missing observations were below 5%. Unit root tests on balanced and unbalanced panel data validated the health convergence hypothesis, showing no unit roots in economic growth rate, current health expenditure, and female and male population indicators (P<0.05). Interestingly, the hypothesis for hospital bed counts in the unbalanced panel, differing from the balanced panel, offers new insights into addressing incomplete health data. Conclusion: While G7 have economic similarities, their health indicators diverge (excluding hospital bed counts). Variations in health indicators stem from healthcare system structures, funding mechanisms, resource allocation, and health investments, even among economies of similar size. Therefore, G7 member states should develop tailored national health policies based on their specific circumstances and priorities, utilizing economic convergence data for effective health resource planning.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".