Ageing Among Off-reserve Indigenous Populations in Canada
Bibliographic record
Abstract
Indigenous populations in Canada experience ageing differently than non-Indigenous populations. The intergenerational effects of colonization on Indigenous Peoples’ health combined with Indigenous populations’ holistic approach to health and ageing warrant the need for Indigenous-specific ageing research. In the past decade there has been an increased focus on Indigenous-specific ageing research. However, there remain many areas of ageing research to be explored. In this thesis, Indigenous and epidemiologic research methods were utilized in combination with a community-engaged approach to fill some of these research gaps. This is the first study to investigate both the physical and emotional health aspects of ageing for off-reserve Indigenous populations in Canada. I collaborated with a Community Advisory Board to develop research questions relevant to Indigenous Peoples, inform my data analysis plan to produce meaningful results and to use Indigenous perspectives to interpret the research findings. Data from the Canadian Community Health Survey were used to estimate the prevalence of and factors associated with frailty among off-reserve Indigenous populations ≥45 years of age. The same survey data was used to study ageing from a more holistic approach by investigating life satisfaction as a measure of successful ageing. The prevalence of frailty was approximately 18% for Indigenous men and 20% for Indigenous women. The age- and sex-adjusted prevalence estimates were higher for both Indigenous men and women compared to non-Indigenous men and women. Although the prevalence of frailty was high for Indigenous populations, 90% of Indigenous people included in this study reported high levels of life satisfaction. A strong sense of community belonging, and physical activity were associated with lower levels of frailty and higher levels of life satisfaction. Recommendations were made based on the findings for community-led policies and programs to promote healthy ageing through access to culturally appropriate health services, land-based exercise programs and facilitating connections between older Indigenous people and their community. In addition to the quantitative research findings, the unique methodology used in this thesis may serve as a guide to approach future Indigenous epidemiological studies, an area that has received little research attention to date.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".