Health, economic growth, and Gini index in North America using a panel model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it