Patients’ perspectives on how to improve diabetes care and self-management: qualitative study
Bibliographic record
Abstract
OBJECTIVE: People living with diabetes need and deserve high-quality, individualised care. However, providing such care remains a challenge in many countries, including Canada. Patients' expertise, if acknowledged and adequately translated, could help foster patient-centred care. This study aimed to describe Expert Patients' knowledge, wisdom and advice to others with diabetes and to health professionals to improve diabetes self-management and care. DESIGN AND METHODS: We recruited a convenience sample of 21 men and women. Participants were people of diverse backgrounds who are Patient Partners in a national research network (hereafter Expert Patients). We interviewed and video-recorded their knowledge, wisdom and advice for health professionals and for others with diabetes. Three researchers independently analysed videos using inductive framework analysis, identifying themes through discussion and consensus. Expert Patients were involved in all aspects of study design, conduct, analysis and knowledge translation. RESULTS: Acknowledging and accepting the reality of diabetes, receiving support from family and care teams and not letting diabetes control one's life are essential to live well with diabetes. 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. CONCLUSION: Expert Patients pointed to a number of areas of improvement in diabetes care that may be actionable individually by patients or health professionals, and also collectively through intergroup collaboration. Improving the quality of care in diabetes is crucial for improving health outcomes for people 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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 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".