Diabetes Attitudes, Wishes and Needs second study (DAWN2™): Cross‐national benchmarking of diabetes‐related psychosocial outcomes for people with diabetes
Bibliographic record
Abstract
AIMS: The second Diabetes Attitudes, Wishes and Needs (DAWN2) study aimed to assess psychosocial outcomes in people with diabetes across countries for benchmarking. METHODS: Surveys included new and adapted questions from validated questionnaires that assess health-related quality of life, self-management, attitudes/beliefs, social support and priorities for improving diabetes care. Questionnaires were conducted online, by telephone or in person. RESULTS: Participants were 8596 adults with diabetes across 17 countries. There were significant between-country differences for all benchmarking indicators; no one country's outcomes were consistently better or worse than others. The proportion with likely depression [WHO-5 Well-Being Index (WHO-5) score ≤ 28] was 13.8% (country range 6.5-24.1%). Diabetes-related distress [Problem Areas in Diabetes Scale 5 (PAID-5) score ≥ 40] was reported by 44.6% of participants (17.2-67.6%). Overall quality of life was rated 'poor' or 'very poor' by 12.2% of participants (7.6-26.1%). Diabetes had a negative impact on all aspects investigated, ranging from 20.5% on relationship with family/friends to 62.2% on physical health. Approximately 40% of participants (18.6-64.9%) reported that their medication interfered with their ability to live a normal life. The availability of person-centred chronic illness care and support for active involvement was rated as low. Following self-care advice for medication and diet was most common, and least common for glucose monitoring and foot examination, with marked country variation. Only 48.8% of respondents had participated in diabetes educational programmes/activities to help manage their diabetes. CONCLUSIONS: Cross-national benchmarking using psychometrically validated indicators can help identify areas for improvement and best practices to drive changes that improve 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".