Regional differences in type 2 diabetes prevention priorities for women with previous gestational diabetes: A multi-methods consensus study
Bibliographic record
Abstract
OBJECTIVES: To identify values, principles, and research priorities for type 2 diabetes mellitus (T2DM) prevention in women with previous gestational diabetes mellitus (GDM) across five regions, and evaluate the appropriateness of modified Delphi and nominal group consensus methods in diverse cultural settings. STUDY DESIGN: Mixed-methods. METHODS: Health professionals and women with previous GDM from five regions were invited to participate in the priority-setting activities according to a modified Delphi process and nominal group technique. The Child Health and Nutrition Research Initiative was used to develop the assessment criteria, which included answerability, effectiveness, deliverability, the maximum potential for improving the health and well-being of postpartum mothers, and the effect on equity. Participants ranked items in three rounds of the Delphi process. Evaluation surveys and semi-structured interviews were conducted to understand participants' experiences of the process. RESULTS: Fifty health professionals and 50 women with previous GDM participated in the priority-setting process and evaluation survey, with 11 individuals also taking part in interviews. Regional differences emerged in priority rankings for values and principles. Africa emphasised cost-effectiveness and capacity building; the Americas prioritised people-centred approaches and continuity of care; Asia focused on equity-driven services and family support; Europe highlighted combating misinformation; Oceania emphasised planning skills. Consensus methods were feasible and acceptable across the regions. CONCLUSION: T2DM prevention priorities for women with a history of GDM vary across geographical regions, suggesting a need for local and tailored approaches for effective implementation. Consensus approaches involving the community in implementation efforts are acceptable across diverse geographical contexts.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".