Clinical care gaps and solutions in diabetes and advanced chronic kidney disease: a patient-oriented qualitative research study
Bibliographic record
Abstract
BACKGROUND: Patients with diabetes and advanced chronic kidney disease face a high health care burden. As part of a patient-oriented research initiative to identify ways to better support patients' diabetes care, we explored their health care experience and solutions for patient-centred diabetes care. METHODS: We engaged 2 patients with advanced kidney disease and diabetes to join our multidisciplinary team as full research partners. They were involved in our design and conduct of the study, the analysis of the results and knowledge translation. We conducted qualitative interviews (1:1 semistructured interviews and focus groups) with patients with a history of both diabetes (type 1 or 2) and advanced kidney disease including those using dialysis. We identified overarching themes using individual and team analysis and conducted interviews until data saturation was reached. RESULTS: Twelve participants were interviewed between October 2017 and February 2018. Six people were interviewed in 2 separate focus groups (consisting of 4 and 2 participants) and 6 participated in 1:1 interviews with our team. Participants described being burdened by medical appointments, strict conflicting diets, costly diabetes therapies and fragmented, siloed health care. They indicated that self-management support, education and coordinated diabetes care might better support their diabetes care. INTERPRETATION: Patients with complex medical comorbidities face many challenges traversing a health care system organized around single diseases. Researchers and policy-makers should study and develop patient-centred diabetes care strategies to better support these high-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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".