Mental health and cultural continuity among an urban Indigenous population in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVES: Mental health and psychiatric disorders significantly affect individuals on personal and social levels. Indigenous populations in Canada have disproportionately high rates of mental health diagnoses. Our Health Counts (OHC) Toronto assessed mental health, racism, family disruption, and cultural continuity among urban Indigenous people. The objectives of this study were to understand (1) the demographics and characteristics of Indigenous adults with a diagnosed psychological/mental health disorder and (2) potential associations of psychological/mental health diagnoses with experiences of colonization and cultural continuity among Indigenous adults in Toronto. METHODS: Using community-based participatory research methods, Indigenous adults in Toronto were recruited by respondent-driven sampling (RDS) to complete a comprehensive health assessment survey. RDS-II weights were applied to calculate population-based estimates, and adjusted odds ratios with 95% confidence intervals were produced using logistic regression, controlling for age and gender. RESULTS: Among Indigenous adults, nearly half (45%) reported receiving a mental health diagnosis. Participants reported lifetime anxiety disorders (53%), major depression (51%), and high rates of suicide ideation (78%). Of Indigenous adults with a diagnosed mental health disorder, 72.7% reported participating in ceremony. Attending residential schools (OR: 7.82) and experiencing discrimination (OR: 2.69) were associated with having a mental health disorder. CONCLUSION: OHC Toronto responded to the gaps in health assessment data for urban Indigenous people. Despite historic and ongoing trauma, Indigenous people have maintained cultural practices and a strong sense of identity. Efforts aimed at supporting Indigenous well-being must respond to the roots of trauma, racism, and existing Indigenous community knowledge and strengths.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".