Patients' perspectives on how to improve diabetes self-management and medical care: a qualitative study
Bibliographic record
Abstract
Background: The experience of living with a chronic disease such as diabetes can provide valuable knowledge about medical care and self-management. Such knowledge may be of use to people seeking to improve diabetes self-management and to health professionals seeking to provide better patient-centered care. Objective: To identify potential areas for improvement in diabetes care from the perspectives of people living with diabetes and their caregivers. Methods: We interviewed 21 people living with diabetes (hereafter called expert patients) who were patient partners in a national Patient-Oriented Research network. Expert patients were men and women from various backgrounds, including Indigenous people and immigrants to Canada. They had significant lived experience of diabetes and were able to offer diverse patient and caregiver perspectives. Three authors independently analyzed videos using inductive framework analysis, identifying themes through discussion and consensus. Results: From expert patients’ perspective, people living with diabetes benefit from acknowledging and accepting the reality of diabetes, receiving support from their family and care team, and not letting diabetes control their lives. To improve diabetes care, health professionals should understand and acknowledge the impact of diabetes on patients and their families, and communicate with patients openly, respectfully, with empathy and cultural competency. Conclusions: From the perspectives of expert patients, there are areas for improvements in diabetes care. These improvements are actionable individually by patients or health professionals and also collectively through collaboration between both groups. Improving the quality of care in diabetes is crucial for improving health outcomes in Canada.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".