An Indigenous and Western paradigm to understand gestational diabetes mellitus: Reflections and insights
Bibliographic record
Abstract
Indigenous women face many barriers to maternal care during pregnancy in Canada. A participatory study was conducted in two First Nations Communities in Nova Scotia, Canada to gain new knowledge about Mi’kmaw women’s experiences of living with gestational diabetes mellitus (GDM). Relational ethics helped guide this journey. In this paper we describe how Indigenous and Western approaches were used to understand Mi’kmaw women’s experiences with GDM. It was important to us that the research methodology facilitated building relationship and trust. This led to an openness and willingness of the women to express their concerns and offer ways to address GDM in their communities. The challenges of blending Indigenous approaches with Western research are also discussed in the paper. The foundational principles that were used during this research included: 1) Staying true to my word; 2) Mutual Trust; 3) Mutual Respect; 4) Being Flexible; 5) Being Non judgemental; 6) Working in partnership; 7) Taking time to explain; 8) Promoting autonomy; and 9) Genuine connectiveness. The findings revealed that the research assisted the Mi’kmaw women in understanding their experiences in new ways and helped to build capacity so that they could take action to improve their health, while sustaining their Mi’kmaw culture.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| 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".