Are there disparities in diabetes care? A comparison of care received by US rural and non-rural adults with diabetes
Bibliographic record
Abstract
Aim Are there differences in diabetes care between rural and non-rural US adults with diabetes? Background Rural Healthy People 2010 includes diabetes as a major health priority, suggesting a possible disparity between diabetes care in rural settings as compared to non-rural locales. Methods This cross-sectional study using population-based survey data sought to determine if there was a difference in the quality of diabetes care between rural and non-rural US adults (⩾18 years). A diabetes care index was computed from five separate dichotomous care-related variables (HbA1c checked, lipids checked, dilated eye exam, feet checked by health care provider, and diabetes education), with adequate care defined as receiving at least four of these interventions. Multivariate methods were used to detect differences in diabetes care received by individuals living in rural compared to non-rural settings. Results Multivariate regression analysis revealed that US adults with diabetes living in rural communities were more likely to receive inadequate care than non-rural residents (OR = 1.205; 95% CI 1.201, 1.209). Rural residents were more likely to receive inadequate diabetes care if they were: <40 years of age, male, Caucasian, not a high school graduate, not partnered, without health insurance, inactive or without an identified health care provider. Those deferring medical care because of cost, or who did not have an annual routine physical or had fewer than two diabetes related office visits annually were also at greater risk for suboptimal care. Routine physical checkups and deferring medical care because of cost had a greater impact on diabetes care for rural adults compared to non-rural adults. Conclusion The results of this study indicated that rural residents were less likely to receive adequate diabetes care compared to their non-rural counterparts. The findings suggest that efforts to identify and to address this disparity would likely improve the outcomes for diabetic individuals living in rural communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".