Relationship Among Diabetes Distress, Decisional Conflict, Quality of Life, and Patient Perception of Chronic Illness Care in a Cohort of Patients With Type 2 Diabetes and Other Comorbidities
Bibliographic record
Abstract
OBJECTIVE The primary outcome is to evaluate the relationship between diabetes distress and decisional conflict regarding diabetes care in patients with diabetes and two or more comorbidities. Secondary outcomes include the relationships between diabetes distress and quality of life and patient perception of chronic illness care and decisional conflict. RESEARCH DESIGN AND METHODS This was a cross-sectional study of 192 patients, ≥18 years of age, with type 2 diabetes and two or more comorbidities, recruited from primary care practices in the Greater Toronto Area. Baseline questionnaires were completed using validated scales: Diabetes Distress Scale (DDS), Decisional Conflict Scale (DCS), Short-Form Survey 12 (SF-12), and Patient Assessment of Chronic Illness Care (PACIC). Multiple linear regression models evaluated associations between summary scores and subscores, adjusting for age, education, income, employment, duration of diabetes, and social support. RESULTS Most participants were >65 years old (65%). DCS was significantly and positively associated with DDS (β = 0.0139; CI 0.00374–0.0246; P = 0.00780). DDS–emotional burden subscore was significantly and negatively associated with SF-12–mental subscore (β =−3.34; CI −4.91 to −1.77; P < 0.0001). Lastly, DCS was significantly and negatively associated with PACIC (β = −6.70; CI −9.10 to −4.32; P < 0.0001). CONCLUSIONS We identified a new positive relationship between diabetes distress and decisional conflict. Moreover, we identified negative associations between emotional burden and mental quality of life and patient perception of chronic illness care and decisional conflict. Understanding these associations will provide valuable insights in the development of targeted interventions to improve quality of life in patients with diabetes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".