“I Do My Best To Listen to Patients”: Qualitative Insights Into DAWN2 (Diabetes Psychosocial Care From the Perspective of Health Care Professionals in the Second Diabetes Attitudes, Wishes and Needs Study)
Bibliographic record
Abstract
PURPOSE: The aim of this study was to describe the perspectives of diabetes care professionals regarding the roles and responsibilities of people with diabetes (PWD), health care professionals (HCPs), and the larger society to improve the provision of person-centered diabetes care. METHODS: The survey contained open-ended items about challenges of, successes of, and wishes for improvements in treating adults with diabetes. All responses were systematically coded using a schema developed and validated through multinational collaboration. FINDINGS: Participants were 4785 diabetes care professionals (physicians, nurses, and dietitians) from 17 countries. The data contained 2 distinct themes. One theme reflected the fact that the roles and responsibilities of HCPs are transitioning from those of one who "tells" to one who "listens" to PWD. Some ways that HCPs can "listen" to PWD and family members is to involve them in goals and to encourage self-management for the improvement of treatment. The second theme identified barriers to successful diabetes care, which include a lack of time and collaboration from HCPs, a lack of availability of resources for treatment, and a lack of psychosocial support. IMPLICATIONS: The views of diabetes care professionals are in transition from a conventional hierarchic approach to a PCC approach. Further adoption of this approach would be facilitated by additional psychosocial training and educational/psychological resources, increased teamwork, and societal changes that would make it easier for people to live successfully 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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".