Diabetes Attitudes, Wishes and Needs second study (DAWN2™): Cross‐national comparisons on barriers and resources for optimal care—healthcare professional perspective
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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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".