Co-Designing a postpartum diabetes prevention program after gestational diabetes mellitus: A MoSCoW prioritization workshop exercise
Bibliographic record
Abstract
AIMS: Avoiding Diabetes After Pregnancy Together with Moms (ADAPT-M) is a postpartum lifestyle modification program aimed at preventing type 2 diabetes mellitus (T2DM) after gestational diabetes mellitus (GDM). The purpose of the workshop was to bring together key stakeholders to support the contextual adaptation of ADAPT-M needed for effective implementation. METHODS: Participants were invited to a co-design workshop held on June 10th, 2024. Participants engaged in facilitator-led breakout groups to prioritize intervention and implementation components using the MoSCoW method. Data were analyzed using a deductive thematic analysis approach. RESULTS: In total, 27 attendees and eight facilitators participated in the workshop. We identified a total of 11 themes distributed across the four MoSCoW categories. Themes included: comprehensive support and education, cultural sensitivity and inclusivity, integrated and personalized care, communication and accessibility, and community and peer support networks, among others. CONCLUSIONS: Our findings support research development that aligns with a core outcome set for diabetes after pregnancy prevention interventions, correlates with current diabetes prevention programs after GDM, and further refines potential changes to ADAPT-M as it is implemented in the real-world setting. Future work can consider these components and co-design methods when developing diabetes prevention programs in high-risk postpartum women with GDM.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".