The Mental Health of First Nations Children in Manitoba: A Population-Based Retrospective Cohort Study Using Linked Administrative Data: La santé mentale des enfants des Premières Nations au Manitoba : une étude de cohorte rétrospective dans la population, à l’aide de données administratives liées
Bibliographic record
Abstract
Objective First Nations children face a greater risk of experiencing mental disorders than other children from the general population because of family and societal factors, yet there is little research examining their mental health. This study compares diagnosed mental disorders and suicidal behaviours of First Nations children living on-reserve and off-reserve to all other children living in Manitoba. Method The research team, which included First Nations and non-First Nations researchers, utilized population-based administrative data that linked de-identified individual-level records from the 2016 First Nations Research File to health and social information for children living in Manitoba. Adjusted rates and rate ratios of mental disorders and suicide behaviours were calculated using a generalized linear modelling approach to compare First Nations children ( n = 40,574) and all other children ( n = 197,109) and comparing First Nations children living on- and off-reserve. Results Compared with all other children, First Nations children had a higher prevalence of schizophrenia (adjusted rate ratio (aRR): 4.42, 95% confidence interval (CI), 3.36 to 5.82), attention-deficit hyperactivity disorder (ADHD; aRR: 1.21, 95% CI, 1.09 to 1.33), substance use disorders (aRR: 5.19; 95% CI, 4.25 to 6.33), hospitalizations for suicide attempts (aRR: 6.96; 95% CI, 4.36 to 11.13) and suicide deaths (aRR: 10.63; 95% CI, 7.08 to 15.95). The prevalence of ADHD and mood/anxiety disorders was significantly higher for First Nations children living off-reserve compared with on-reserve; in contrast, hospitalization rates for suicide attempts were twice as high on-reserve than off-reserve. When the comparison cohort was restricted to only other children in low-income areas, a higher prevalence of almost all disorders remained for First Nations children. Conclusion Large disparities were found in mental health indicators between First Nations children and other children in Manitoba, demonstrating that considerable work is required to improve the mental well-being of First Nations children. Equitable access to culturally safe services is urgently needed and these services should be self-determined, planned, and implemented by First Nations people.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".