Diabetes Attitudes, Wishes and Needs second study (DAWN2™): Cross‐national comparisons on barriers and resources for optimal care—healthcare professional perspective
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: The second Diabetes Attitudes, Wishes and Needs (DAWN2) study sought cross-national comparisons of perceptions on healthcare provision for benchmarking and sharing of clinical practices to improve diabetes care. METHODS: In total, 4785 healthcare professionals caring for people with diabetes across 17 countries participated in an online survey designed to assess diabetes healthcare provision, self-management and training. RESULTS: Between 61.4 and 92.9% of healthcare professionals felt that people with diabetes needed to improve various self-management activities; glucose monitoring (range, 29.3-92.1%) had the biggest country difference, with a between-country variance of 20%. The need for a major improvement in diabetes self-management education was reported by 60% (26.4-81.4%) of healthcare professionals, with a 12% between-country variance. Provision of diabetes services differed among countries, with many healthcare professionals indicating that major improvements were needed across a range of areas, including healthcare organization [30.6% (7.4-67.1%)], resources for diabetes prevention [78.8% (60.4-90.5%)], earlier diagnosis and treatment [67.9% (45.0-85.5%)], communication between team members and people with diabetes [56.1% (22.3-85.4%)], specialist nurse availability [63.8% (27.9-90.7%)] and psychological support [62.7% (40.6-79.6%)]. In some countries, up to one third of healthcare professionals reported not having received any formal diabetes training. Societal discrimination against people with diabetes was reported by 32.8% (11.4-79.6%) of participants. CONCLUSIONS: This survey has highlighted concerns of healthcare professionals relating to diabetes healthcare provision, self-management and training. Identifying between-country differences in several areas will allow benchmarking and sharing of clinical practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it