A decolonizing approach in population health research: examining the impacts of the federal out for confinement policy on maternal and child outcomes in First Nation communities in Manitoba
Bibliographic record
Abstract
Background: This dissertation documents a journey of an Anishinaabekwe student into the space where western and Indigenous methodologies meet. The Truth and Reconciliation Commission of Canada’s (TRCC) Call to Action #19, challenges all levels of government to close the gap in measurable maternal and child outcomes. This study employs a decolonizing framework to examine the impact of the federal policy of evacuating women residing in rural and isolated First Nation communities in Manitoba to urban centres to deliver their babies. The overarching goal was to demonstrate how a decolonizing framework and positioning traditional Knowledge Keepers and knowledge at the forefront can inform quantitative research. Approach and Methods: Decolonizing elements embedded within an Indigenous framework framed the retrospective cohort study by employing an Indigenous framework, a Council of Grandmothers/Knowledge Keepers/Grandmother Advisor to guide the research. A cohort comprising low-risk women residing in Manitoba First Nation communities who delivered a baby between the years 2005-2015 was assembled to compare women who travelled for birth and were subject to the “Out for Confinement” (OFC) policy (the practice of sending women out of their communities for birth) to those women who did not. Population-level administrative data housed at the Manitoba Centre for Health Policy were analyzed using logistic regression models to assess the association between the OFC policy on maternal, infant and child outcomes. Results: Utilizing an Indigenous framework, this study generated evidence that the present-day colonial OFC policy harms Indigenous women, families, and communities. The OFC policy was associated with increased 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 0.43, 0.36, 0.51), after adjusting for various confounders. Conclusion: To answer the TRCC 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 robust. Decolonizing and Indigenous frameworks are feasible and essential in population health research and do not detract from scientific rigor.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".