An Analysis of the Health Performance of G7 Countries: An Application Using the LPI-HI-Based DNMA Method
Bibliographic record
Abstract
Aim: The purpose of this study is to analyze the health performance of G7 countries. Materials and Methods: In the study, the health performances of G7 countries were measured using the most recent and up-to-date 2023 Legatum Prosperity Index-Health Indicator (LPI-HI) data and the LPI-HI-based DNMA multi-criteria decision-making method (MCDM). Results: The health performance rankings of the countries, based on the LPI-HI-based DNMA method, have been identified as France, UK, Canada, Italy, USA, Germany, and Japan. It was also observed that only France and the UK have health performances exceeding the average performance value. Additionally, according to the sensitivity analysis of the LPI-HI based DNMA MCDM method, it was found to be sensitive; according to the comparative analysis, it was credible and reliable; and according to the simulation analysis, it was robust and stable. Conclusion: It is believed that Canada, Italy, the USA, Germany, and Japan, which have health performances below the average value, need to improve their health performance to contribute more significantly to the global economy. Methodologically, the sensitivity, comparative, and simulation analysis results indicate that the health performances of G7 countries can be measured using the 2023 LPI-HI criteria with the LPI-HI-based DNMA method.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".