The health of Indigenous peoples living in Canada: Understanding distal, intermediate and proximal determinants of health
Bibliographic record
Abstract
Understanding how proximal, intermediate, and distal determinants of Indigenous peoples’ health in Canada, relate to the physical and mental health of First Nations, Métis, and Inuit, can shed light on how to allocate health-related resources to address well documented health disparities in these groups. This dissertation contributes to the literature by addressing two population-level quantitative research questions pertaining to Indigenous peoples’ physical and mental health, and a qualitative case study examining what factors maintain and improve Indigenous community health workers’ mental wellness and access to mental health supports. First, this thesis establishes a link between being Indigenous and health-related quality of life using multivariate regressions, as well as decomposition techniques. Second, the relationship between having an ancestor who survived the Residential School System, and five physical and mental health outcomes, controlling for determinants of health are estimated using multivariate ordered logistic and logistic regressions. Third, given that Indigenous self-government is an important determinant of health and wellbeing, an explanatory single-case study design is used to explore what factors maintain and improve, or create barriers to mental wellness and access to mental health supports for Indigenous community health workers in an Indigenous-governed health system. These chapters build on each other, and use a variety of methodological approaches, to identify if and to what degree observable determinants of health account for the physical and mental health of Indigenous peoples living in Canada. Substantively, this thesis evaluates empirically, the relationship between determinants of health and health outcomes for Indigenous peoples. Findings could be used to advocate for adequate and sustained investment in programs and services responsive to the contexts and needs of Indigenous men and women living in Canada. Methodologically, novel applications of statistical/econometric methodologies, furthers understanding of quantitative relationships examined with respect to Indigenous peoples’ physical and mental health at the population-level. In terms of a theoretical contribution, this dissertation contributes by lending further insight into the empirical relationships between determinants of Indigenous peoples’ health and health outcomes, and by introducing a framework for conceptualizing factors that strengthen mental wellness of Indigenous community health workers in remote Northern contexts in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".