A Decolonizing Approach in Population Health Research: Examining the Association between the federal maternal evacuation policy on Maternal and Child outcomes in First Nation (FN) Communities in Manitoba.
Bibliographic record
Abstract
ObjectivesResponding to the Truth and Reconciliation Commission of Canada’s (TRC) Call to Action #19 to close the gap in maternal/child outcomes, the goal of this study was to provide a baseline for select outcomes and demonstrate how an Indigenous/decolonizing framework can be applied to population health research involving Indigenous people. ApproachThis retrospective cohort study was embedded within a decolonizing and Indigenous framework. Data extracted from administrative data housed at the Manitoba Centre for Health Policy was utilized to create a cohort of low-risk women residing in FN communities delivering a baby between 2005-2015. Two groups of mother/child dyads were compared: those evacuated for birth and those who were not required to leave home. Data were analyzed to assess the association between the evacuation policy on health outcomes. ResultsDecolonizing and Indigenous frameworks are feasible, essential, and necessary in population health research involving Indigenous people. This methodology does not detract from scientific rigor. In keeping with Indigenous methodology, Knowledge Keepers and a Grandmother Advisor informed the research from the onset, including insightful dialogue about the study findings. Using such an approach, this study generated evidence that the present-day OFC policy continues to harm Indigenous women, families, and communities. The OFC policy is associated with increased odds of inadequate PNC (OR 1.64 1.51, 1.79 CI) and small for gestational age births (OR 1.25 1.02, 1.50 CI) and decreased breastfeeding initiation (OR 0.55 0.50, 0.61 CI) and maternal psychological distress diagnoses (OR .43 0.36, 0.51), after adjusting for various confounders. ConclusionThis study documented a journey of an Anishinaabekwe in the space where western and Indigenous methodologies met. In answering the TRC call to improve maternal and infant outcomes, epidemiological and population health research requires epistemological frameworks that adequately incorporate the voices and realities of Indigenous people's lives while remaining scientifically rigorous.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".