Canada's residential school system: measuring the intergenerational impact of familial attendance on health and mental health outcomes
Bibliographic record
Abstract
BACKGROUND: We estimate the intergenerational relationship between the residential school (RS) attendance of an older generation family member and the physical and mental health of a younger generation. METHODS: Data from the 2012 Aboriginal Peoples Survey (APS) is used to examine the relationship between previous generational family RS attendance and the current physical and mental health of off-reserve First Nations, Métis and Inuit Canadians. Five outcomes are considered (self-perceived health, mental health, distress, suicidal ideation and suicide attempt). Direct (univariate) and indirect (multivariate) effects of family RS attendance are examined for each dependent variable. We draw from the general and indigenous-specific social determinants of health literature to inform the construction of our models. RESULTS: Familial RS attendance is shown to affect directly all five health and mental health outcomes, and is associated with lower self-perceived health and mental health, and a higher risk for distress and suicidal behaviours. Background, mediating and structural-level variables influence the strength of association. Odds of being in lower self-perceived health remain statistically significantly higher with the presence of familial attendance of RS when controlling for all covariates. The odds of having had a suicide attempt within the past 12 months remain twice as high for those with familial attendance of RS. CONCLUSIONS: Health disparities exist between indigenous and non-indigenous Canadians, an important source of which is a family history of RS attendance. This has implications for clinical practice and Canadian public health, as well as countries with similar historical legacies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".