Our Children, Our Future: The Health and Well-being of First Nations Children in Manitoba, Canada.
Bibliographic record
Abstract
ObjectivesGiven the impact of colonization and responding to Canada’s Truth and Reconciliation Commission, we aimed to provide baseline measures of First Nations children’s health and social outcomes in Manitoba, Canada. We also aimed to create a research process where Indigenous and non-Indigenous researchers work collaboratively and in culturally safe ways. ApproachWe formed a team consisting of members of First Nation organizations and academic researchers. Knowledge Keepers from Anishinaabe, Cree, Anishininew, Dakota and Dene Nations guided the study, interpreted results and ensured meaningful knowledge translation. This retrospective cohort study utilized population-based health, social services, education and justice administrative data that allowed de-identified individual-level linkages across all databases through a scrambled health number. Adjusted rates and rate ratios were calculated using a generalized liner modeling approach to compare First Nations children (n=61,726) and all other Manitoba children (n=279,087) and comparing First Nations children living on and off-reserve. ResultsLarge disparities between First Nations and other Manitoba children were found in birth outcomes, physical and mental health, health services, education, social services, justice system involvement and mortality. First Nations infants had higher rates of preterm births, large-for-gestational-age births, newborn readmissions to hospital and lower rates of breastfeeding initiation compared with other Manitoba infants. Suicide rates among First Nations adolescents were ten times higher than among other adolescents in Manitoba, yet we found few differences in diagnosis of mood and anxiety disorders between the groups. First Nations children were also seven times more likely to apprehended by child protection services and youth were ten times more likely to be criminally accused. Knowledge Keepers offered their perspectives on these findings. ConclusionThese findings demonstrate that an enormous amount of work is required in virtually every area – health, social, education and justice – to improve First Nations children’s lives. There is an urgent need for equitable access to services, 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".