Preventing diabetes after pregnancy with gestational diabetes in a Cree community: an inductive thematic analysis
Bibliographic record
Abstract
INTRODUCTION: Historical and political factors underpin the disproportional burden of type 2 diabetes mellitus (T2DM) and gestational diabetes mellitus (GDM) in women, a harbinger of future T2DM, in Indigenous populations. There is a need for T2DM prevention strategies driven by the voices of Indigenous women. In this study, we aimed to understand the perspectives of Cree women with prior GDM living in northern Quebec, where over a quarter of pregnancies are complicated by GDM. RESEARCH DESIGN AND METHODS: A local healthcare worker invited women with GDM in the prior 5 years to participate in semistructured interviews. A Cree-origin research partner and a researcher jointly conducted interviews in-person or by teleconference. Open-ended questions addressed GDM experience, maintaining a healthy lifestyle, and needs/preferences pertinent to designing a T2DM prevention program aimed at women affected by GDM. We adopted an inductive thematic analysis framework to categorize experiences and opinions. RESULTS: Among the 13 mothers interviewed, some success with health behavior changes during pregnancy was reported but there were difficulties postpartum resulting from time constraints, costs of healthy foods, discomfort at the gym related to not being perceived as athletic, and safety concerns. They acknowledged the existence of programs addressing T2DM prevention in their community but did not participate. They endorsed preferences for group sessions, with family collaboration and childcare, that addressed healthy cooking and physical activity and incorporated traditional elements. CONCLUSION: Cree mothers with a history of GDM highlighted several barriers to diabetes prevention. We are working to address these barriers through the creation of a Cree-facilitator-led community-based intervention.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".