Diabetes‐related complications: Which research topics matter to diverse patients and caregivers?
Bibliographic record
Abstract
BACKGROUND: Diabetes is a chronic disease with increasing prevalence worldwide. Although research has improved its treatment and management, little is known about which research topics matter to people living with diabetes, particularly among under-represented groups. OBJECTIVES: To explore the importance of research topics among a diverse range of people living with any type of diabetes or caring for someone living with any type of diabetes. METHODS: We used a convergent mixed-method design with quantitative and qualitative aspects. We surveyed a national sample of people living with diabetes and caregivers of people with diabetes, asking them to rate the importance of 10 predetermined important research topics. We also held three focus groups in two major cities to explore research concerns of people who are under-represented in research. RESULTS: 469 adults (57% men, 42% women) in Canada completed the online survey, indicating that all 10 areas of research mattered to them, with the highest ratings accorded to preventing and treating kidney, eye and nerve complications. Fourteen individuals participated in three focus groups and similarly noted the importance of research on those three complications. Additionally, focus group participants also noted the importance of research around daily management. No new topics were identified. CONCLUSIONS: This study confirmed the importance of research topics among a population of people living with or caring for someone with diabetes. Findings from this study were used to inform the vision for Diabetes Action Canada-a pan-Canadian Strategy for Patient-Oriented Research (SPOR) Network on diabetes and its complications.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".