A Comparative Analysis of the Health Status of Hispanic Children: The Cases of Washington State and Arizona
Bibliographic record
Abstract
abstract: ABSTRACT\n\nFor the last quarter century, Washington State has been ranked in the top third of the United States in health status while Arizona has been consistently around the bottom third. This gap can be partly explained by data related to traditional determinants of health like education, income, insurance rates and income. Moreover, Washington State invests three times more resources in the public health sector than Arizona. Surprisingly, however, Hispanic children in Washington State have poorer health status than Hispanic children in Arizona. This dissertation explores possible explanations for this unexpected situation, using as a conceptual framework the cultural competency continuum developed by Cross.\n\nThe study consisted of analysis of health-related data from Washington State and Arizona, and interviews with state health administrators, local health departments, community-based organizations and university administrators in both states. This research makes a modest contribution to the role that cultural competence plays in the development and implementation of health policy and programs, and the potential impact of this approach on health status. The dissertation ends with recommendations for health policy-makers and program planners, particularly in states with a significant proportion of minority groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".