Avoiding Diabetes After Pregnancy Together with Moms (ADAPT-M): Bridging care from the third to the fourth trimester
Bibliographic record
Abstract
Introduction: Gestational diabetes mellitus (GDM) is a temporary condition that occurs during pregnancy. Patients with GDM are at an increased risk of future type 2 diabetes (T2DM). The maternal care setting thus provides a critical window of opportunity for T2DM prevention. Clinical practice guidelines recommend postpartum follow-up for patients with GDM for T2DM screening and prevention through physical activity and diet counselling. However, implementation of these recommendations has been suboptimal, and approximately 20% of patients with GDM are diagnosed with T2DM within 10 years of delivery. To address this healthcare gap, we co-designed a 6-month intervention tailored for postpartum patients with GDM with input from patients with lived experience and providers. The Avoiding Diabetes after Pregnancy Together with Moms (ADAPT-M) bridges prenatal and postpartum primary care and uses evidence-based health coaching practices for lifestyle modifications. This program was assessed in a pragmatic, randomized control trial within four Central Toronto healthcare centres and showed promising results for feasibility and acceptability. Aims: Working with a team of patients, providers, researchers and health administrators, this study aims to: 1) co-design an implementation and evaluation strategy for ADAPT-M that is adaptable to local contexts; 2) iteratively adapt and sequentially implement ADAPT-M in 3 jurisdictions in Ontario, Canada; and 3) determine the effectiveness of the implemented ADAPT-M program on quintuple aim outcomes to support provincial and national spread and scale. Implementation and Evaluation Plan: We will use a type 3 hybrid effectiveness-implementation design to address our aims. The Consolidated Framework for Implementation Research (CFIR) framework will guide the planning and execution of the study and the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework will guide the evaluation of the implementation effectiveness. A step-wedged multiple-baseline quasi-experimental design will be employed using healthcare administrative, clinical, and mixed-methods data sources. Using this design, we will iteratively evaluate the implementation and explore the real-world effectiveness of the program. Implications: The implementation and evaluation of the ADAPT-M program will allow us to advance the quintuple aim of health improvement, with specific attention to adaptations necessary to meet the needs of equity deserving populations. ADAPT-M will improve population health and healthcare costs through reduced risk of T2DM; patient experience by enhancing self-care support for mothers with GDM; and provider experience by facilitating transition of prenatal to post-partum diabetes care. As many marginalized groups are disproportionately affected by GDM, the ADAPT-M program will advance health equity through an accessible program with culturally adaptable patient resources and tools. *On behalf of the ADAPT-M team: Alfonsi J, Banwatt S, Berger H, Bhatia S, Caplan L, de Souza R, Feig D, Fine R, Fitzpatrick S, Fleming K, Gordon J, Harvey P, Kabasele K, Kastner M, Kwan C, Liutkus J, Mackenzie H, Mian S, Nerenberg K, Parikh A, Phillips A, Price J, Ramdass S, Rayner J, Reid R, Shah B, Sivalogarajah S, Strome J, Wodchis W, Zenlea I
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".