Ethnic belonging and chronic disease in Indigenous populations in Canada
Bibliographic record
Abstract
OBJECTIVES: Indigenous peoples in Canada endure health inequalities and cultural erosion due to colonial legacies. This study examines the relationship between ethnic belonging and chronic disease patterns among three Indigenous groups: First Nations, Inuit, and Métis. METHODS: We analyzed data from the 2017 Indigenous Peoples Survey of Canada, performing latent class analysis to identify distinct classes among 12 chronic disease indicators. We used multinomial logistic regression to examine the relationship between ethnic belonging and subtypes of chronic diseases, also employing average marginal effects to interpret heterogeneity. All analyses incorporated complex survey weights to ensure national representativeness. RESULTS: The final sample comprised 19,621 individuals. Four distinct subgroups were identified: Relatively Healthy, Physical Illness, Mental Illness, and Severe Illness groups. Descriptive statistics revealed that up to 35.0 % of the Indigenous population is in a suboptimal health state. Regression outcomes demonstrated that a strong sense of cultural belonging significantly reduces the odds of both Mental Illness (OR = 0.82, 95 % CI [0.76,0.88]) and Severe Illness (OR = 0.92, 95 % CI [0.84,0.99]). Heterogeneity analyses revealed that the positive association between belonging and health outcomes was stronger in the adult age group, among men, and within First Nations and Inuit groups. CONCLUSION: This study underscores the critical role of ethnic belonging in enhancing health among Indigenous populations, particularly in reducing odds associated with mental and severe health conditions. Policies and community practices should focus on strengthening Indigenous peoples' community belonging and cultural connections.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".