Lack of insurance coverage for testing supplies is associated with poorer glycemic control in patients with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Public insurance for testing supplies for self-monitoring of blood glucose is highly variable across Canada. We sought to determine if insured patients were more likely than uninsured patients to use self-monitoring and whether they had better glycemic control. METHODS: We used baseline survey and laboratory data from patients enrolled in a randomized controlled trial examining the effect of paying for testing supplies on glycemic control. We recruited patients through community pharmacies in Alberta and Saskatchewan from Nov. 2001 to June 2003. To avoid concerns regarding differences in provincial coverage of self-monitoring and medications, we report the analysis of Alberta patients only. RESULTS: Among our sample of 405 patients, 41% had private or public insurance coverage for self-monitoring testing supplies. Patients with insurance had significantly lower hemoglobin A(1c) concentrations than those without insurance coverage (7.1% v. 7.4%, p = 0.03). Patients with insurance were younger, had a higher income, were less likely to have a high school education and were less likely to be married or living with a partner. In multivariate analyses that controlled for these and other potential confounders, lack of insurance coverage for self-monitoring testing supplies was still significantly associated with higher hemoglobin A(1c) concentrations (adjusted difference 0.5%, p = 0.006). INTERPRETATION: Patients without insurance for self-monitoring test strips had poorer glycemic control.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".