The multinational second Diabetes, Attitudes, Wishes and Needs study: results of the French survey
Bibliographic record
Abstract
AIM: The second Diabetes, Attitudes, Wishes and Needs (DAWN2™) multinational cross-sectional study was aimed at generating insights to facilitate innovative efforts by people with diabetes (PWD), family members (FMs), and health care professionals (HCPs) to improve self-management and psychosocial support in diabetes. Here, the French data from the DAWN2™ study are described. METHODS: In France, 500 PWD (80 with type 1 diabetes [T1] and 420 with type 2 diabetes [T2]), 120 FMs, and 288 HCPs were recruited. The questionnaires assessed the impact of diabetes on quality of life and mood, self-management, attitudes/beliefs, and care/support. RESULTS: Diabetes negatively impacted the emotional well-being of 59% of people with T1 versus 45% of people with T2 (P<0.05) and about half of FMs. A high level of distress was felt by about half of PWD and FMs. About half of HCPs reported assessing depression in their patients. Sixty-two percent of FMs considered managing diabetes to be a burden. Hypoglycemia was a source of concern for 64% of people with T1 and 73% of FMs of insulin users. About two-thirds of non-insulin-medicated people with T2 agreed to start insulin if prescribed, while half of HCPs preferred to delay insulin initiation. A discrepancy between HCPs' perceptions of their interactions with their patients and PWD's recollection of these interactions with regard to patients' personal needs and distress was also observed. CONCLUSION: While distress remains under-assessed by HCPs, the negative impact of diabetes on the lives of PWD and FMs clearly induces distress on both groups. These findings provide new understanding of barriers precluding optimal management of diabetes. Developing strategies to overcome these barriers is now warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".