Understanding the Determinants of Health for People With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: We assessed which of a broad range of determinants of health are most strongly associated with health-related quality of life (HRQL) among people with type 2 diabetes. METHODS: Our analysis included respondents from the Canadian Community Health Survey Cycle 1.1 (2000-2001) who were aged 18 years and older and who were identified as having type 2 diabetes. We used regression analyses to assess the associations between the Health Utilities Index Mark 3 and determinants of health. RESULTS: Comorbidities had the largest impact on HRQL, with stroke (-0.11; 95% confidence interval [CI] = -0.17, -0.06) and depression (-0.11; 95% CI = -0.15, -0.06) being associated with the largest deficits. Large differences in HRQL were observed for 2 markers of socioeconomic status: social assistance (-0.07; 95% CI=-0.12, -0.03) and food insecurity (-0.07; 95% CI=-0.10, -0.04). Stress, physical activity, and sense of belonging also were important determinants. Overall, 36% of the variance in the Health Utilities Index Mark 3 was explained. CONCLUSION: Social and environmental factors are important, but comorbidities have the largest impact on HRQL among people with type 2 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".