Trends in socioeconomic disparities in health care quality in four countries
Bibliographic record
Abstract
OBJECTIVE: To provide a targeted portrait of socioeconomic disparities in health care quality in four countries and how those disparities have changed over time. DESIGN: Within each country, comparisons between the highest and lowest quintiles of socioeconomic status were made to determine if disparities exist and if any observed disparities have been decreasing over a 5-year period. SETTING: Small geographic areas in Canada, England, New Zealand and the United States. DATA SOURCES: Data were obtained by working with national health statistics agencies in each country. RESULTS: There were socioeconomic disparities in health care quality and health status for most of the indicators studied in all four countries. The analysis included nine quality indicators in four countries, for a total of thirty-six observations. Twenty-six observations had a ratio of highest to lowest socioeconomic quintile of <0.95 or >1.05. These disparities generally persisted over time. The relative difference between the highest and lowest quintile decreased over time in eight of the twenty-one observations with time-series data available. CONCLUSION: The fact that disparities in a variety of indicators exist in four very different health systems underscores the importance of factors common to the four systems or factors outside the health system. Some successful strategies for reducing disparities could potentially be learned from the few examples of success in these countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".