Health disparities and determinants of health: A glance at Healthy People 2020 goals
Bibliographic record
Abstract
Introduction: Adverse health outcomes are often used as indicators of the health of a nation and are generally better in developed countries. According to the World Health Organization, every day, about 800 women died due to complications of pregnancy and child birth. Almost all of these deaths occurred in low-resource settings, and most could have been prevented. Maternal mortality ratio in the United States in 2015 was 14 maternal deaths per 1000 live births, range, significantly higher than most developed countries including Sweden (4 per 1000 live births), Switzerland (4 per 1000 live births), Austria (4 per 1000 live births), Japan (5 per 1000 live births), Germany (6 per 1000 live births), Canada (7 per 1000 live births), France (8 per 1000 live births), United Kingdom (9 per 1000 live births). Methods: Health outcomes were collected from the Centers for Disease Control and Prevention while socio-economic related indicators were extracted from the US Census Bureau. Selected health outcomes in the study are: infant and fetal mortality, maternal mortality, life expectance and cancer. Socio-economic indicators such as poverty and health insurance coverage were also analyzed. An evaluation of health disparities among racial and ethnic groups was performed. Correlation analyses were conducted to explore the potential strength of the relationship between health outcomes and socioeconomic factors in the US at the state level. Conclusion: Health disparities are still a major public health problem in US. A strong correlation at the state level between health outcomes and poverty and health insurance coverage at the state level was identified.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".