Social Determinants of Mental Health and Well-Being among Aboriginal Peoples in Canada
Bibliographic record
Abstract
The articles in this volume address the question: How do social determinants structure the health and well-being of the Aboriginal population in Canada? The first article uses bivariate statistical tests to assess whether First Nations residents’ subjective assessments of personal and community well-being correspond to scores from the Community Well-Being (CWB) Index, which is a measure of socioeconomic conditions in the community. The second article uses path analysis to test the extent to which the stress process model explains the social distribution of psychological distress and well-being in the off-reserve Aboriginal population. Specifically, it investigates whether stress, mastery, and social support mediate the pathways between social status and mental health outcomes. The results of these two chapters suggest that education is an important determinant of well-being. The final two chapters explore potential barriers to higher educational attainment. The third article uses multinomial logistic regression to assess whether intergenerational exposure to residential schools patterns educational attainment across three cohorts of First Nations adults. The fourth chapter uses churn theory to examine whether mobility patterns affect educational attainment among Aboriginal adolescents and young adults living off-reserve.\nCollectively, the articles presented here support the contention that the health and well-being of the Aboriginal population is tied to, although not solely determined by, their social and economic positions. CWB scores were significantly associated with residents’ perceptions of their own and community’s well-being. Stress, mastery, and social support are mediators between social status and mental health; however, it appears that specific social statuses are more strongly associated with specific mediators. In terms of educational attainment, there was no clear patterning based on intergenerational proximity or density of residenial school exposure. The results suggest that exposure alone is a poor predictor. Patterns of migration were associated with educational attainment in both cohorts. Generally, it appears that frequent moves are associated with higher odds of dropping out or falling behind in high school. However, having never moved is associated with lower odds of attending post-secondary.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".