Diabetes Attitudes, Wishes and Needs second study (DAWN2™): Cross‐national benchmarking indicators for family members living with people with diabetes
Bibliographic record
Abstract
AIMS: The second Diabetes Attitudes, Wishes and Needs (DAWN2) study examined the experiences of family members of people with diabetes for benchmarking and identifying unmet needs or areas for improvement to assist family members and those with diabetes to effectively self-manage. METHODS: In total, 2057 family members of people with diabetes participated in an online, telephone or in-person survey designed to assess the impact of diabetes on family life, family support for people with diabetes and educational and community support. RESULTS: Supporting a relative with diabetes was perceived as a burden by 35.3% (range across countries 10.6-61.7%) of respondents. Over half of respondents [51.4% (22.5-76.0%)] rated their quality of life as 'good' or 'very good'. However, distress about the person with diabetes was high, with 61.3% (31.5-86.4%) worried about hypoglycaemia. The impact of diabetes on aspects of life was felt by 51.8% (46.9-58.6%). The greatest negative effect was on emotional well-being [44.6% (31.8-63.0%)], although depression was less common [11.6% (4.2-20.0%)]. Many respondents did not know how to help the person with diabetes [37.1% (17.5-53.0%)] and wanted to be more involved in their care [39.4% (15.5-61.7%)]. Participation in diabetes educational programmes was low [23.1% (9.4-43.3%)], although most of those who participated found them helpful [72.1% (42.1-90.3%)]. CONCLUSIONS: Diabetes has a negative impact on family members of people with diabetes. DAWN2 provides benchmarking indicators of family members' psychosocial needs that will help identify the support required for, and from, them to improve the lives of people with diabetes and their families.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".