International Comparison of Health Resource Utilization in Subjects With Diabetes
Bibliographic record
Abstract
OBJECTIVE: To compare health resource utilization in patients with diabetes between the U.S. and Canada. RESEARCH DESIGN AND METHODS: We combined measures of health care utilization, personal, demographic, health status, functional status, and comorbid conditions from the current National Health Interview Survey (U.S.) and the National Public Health Survey (Canada). A binary logistic regression analysis was used to examine how country of residence influences the probability of accessing health care resources in adult Caucasian subjects after controlling for potential confounders. RESULTS: Subjects from Canada (n = 521) were more likely to have contact with a general physician (odds ratio [OR] 4.01, 95% CI 2.26-7.14), eye specialists (1.46, 1.08-1. 98), and any physician (3.02, 1.03-8.84) in the past year than their American counterparts (n = 825) but were less likely to have had contact with other medical specialists (0.33, 0.24-0.46). Subjects in Canada were also more likely to have been hospitalized overnight (1.79, 1.17-2.75) or to have contact with a health care professional in the previous 12 months (3.35, 1.01-12.81). CONCLUSIONS: Significant disparities exist in health service utilization for adult Caucasian individuals with diabetes in Canada versus the U.S. after controlling for various confounders. From what is known regarding optimal treatment of diabetes, those with diabetes in the U.S. have a greater chance of not receiving recommended care.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".