Educational Attainment is Associated with Health Care Utilization and Self-Care Behavior by Individuals with Diabetes
Bibliographic record
Abstract
The aim of this study was to examine the association between educational attainment and utilization of the health care system and self-care behavior by individuals with diabetes.The Barriers to Diabetes Care Survey asked individuals with diabetes about their care.The questionnaire was completed using computer-assisted telephone interviewing techniques.Participants were found by random digit dialing across Ontario, Canada (eligible n = 1,031).We dichotomized educational attainment at high school.We examined the relationship of educational attainment with measures of health care system utilization and of self-care behavior.We adjusted for age, sex, income, health insurance status, and diabetes type, duration and treatment regimen.Individuals with high educational attainment were more likely to have had an ophthalmologic examination during the previous year (odds ratio 1.37, 95% confidence interval 1.04-1.82),and were more likely to report having a specialist (OR 2.08, 95% CI 1.31-3.31)or other paramedical professional (OR 1.91, 95% CI 1.19-3.07)as their most responsible provider of care, rather than a family doctor.Smoking (OR 0.64, 95% CI 0.45-0.90)and blood sugar monitoring (OR 0.70, 95% CI 0.50-0.98)were associated with low educational attainment, while following a meal plan was associated with high educational attainment (OR 1.39, 95% CI 1.07-1.80).Since appropriate utilization of the health care system and self-care behavior are essential for diabetes management, our findings suggest that people with low educational attainment are independently at risk for worse diabetes care.Health care providers should ensure that their communications, teaching materials and instructions are suitable for these higher-risk patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".