Health, economic growth, and Gini index in North America using a panel model
Bibliographic record
Abstract
Objective: The objective of this paper is assessed the nexus among health status, economic growth, and the Gini index in North America and its countries using a panel model. Materials and Method: The materials consist of annual data regarding life expectancy, government health expenditure as percentage of the gross domestic product, Gini index, and gross domestic product at constant 2015 US$ for the period 2000-2019. The method applies a panel model for North America and its three countries: Canada, Mexico and The United States. North America diversity treatment among countries is dealt with fixed and random effects. Results: North America inhabitants health status are negatively influenced by an increasing income inequality, and a reduction on economic growth. The country that expends more in health care is The United States, follow by Canada and Mexico. The biggest reduction on life expectancy from an increase in income inequality is in The United States, followed by Canada and Mexico. Life expectancy increases when Canada and The United States experience economic growth. The countries with inarticulate health policy responses to an increase in income inequality are first Mexico followed by The United States. Conclusions: In North America and its countries an increasing income inequality reduces life expectancy, and government health expenditure. Economic growth benefits life expectancy and government health expenditure. Health status seems to improve with a reduction in income inequality and a greater public health expenditure. Therefore, policies that increases income inequality and reduces public health expenditure seems to be advocates of a reduction: in health status, population welfare and economic growth.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".