Wellbeing of Indigenous Peoples in Canada, Aotearoa (New Zealand) and the United States: A Systematic Review
Bibliographic record
Abstract
Despite the health improvements afforded to non-Indigenous peoples in Canada, Aotearoa (New Zealand) and the United States, the Indigenous peoples in these countries continue to endure disproportionately high rates of mortality and morbidity. Indigenous peoples’ concepts and understanding of health and wellbeing are holistic; however, due to their diverse social, political, cultural, environmental and economic contexts within and across countries, wellbeing is not experienced uniformly across all Indigenous populations. We aim to identify aspects of wellbeing important to the Indigenous people in Canada, Aotearoa and the United States. We searched CINAHL, Embase, PsycINFO and PubMed databases for papers that included key Indigenous and wellbeing search terms from database inception to April 2020. Papers that included a focus on Indigenous adults residing in Canada, Aotearoa and the United States, and that included empirical qualitative data that described at least one aspect of wellbeing were eligible. Data were analysed using the stages of thematic development recommended by Thomas and Harden for thematic synthesis of qualitative research. Our search resulted in 2669 papers being screened for eligibility. Following full-text screening, 100 papers were deemed eligible for inclusion (Aotearoa (New Zealand) n = 16, Canada n = 43, United States n = 41). Themes varied across countries; however, identity, connection, balance and self-determination were common aspects of wellbeing. Having this broader understanding of wellbeing across these cultures can inform decisions made about public health actions and resources.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".