A national-level examination of First Nations peoples’ mental health data: Predicting mental well-being from social determinants of health using the 2017 Aboriginal Peoples Survey
Bibliographic record
Abstract
Introduction: A history of colonization and assimilation have resulted in social, economic, and political disparities for Indigenous people in Canada. Decades of discriminatory policies (e.g., the Indian Act, the Residential School System) have led to numerous health and mental health inequities, which have been intergenerationally maintained. Four main social determinants of health (i.e., income, education, employment, and housing) disproportionately influence the health of Indigenous peoples. These four social determinants have also been used within the Community Well-Being (CWB) index, which assesses the socio-economic wellbeing of a community. This study sought to extend previous research by assessing how specific indicators of CWB predict self-reported mental wellbeing within First Nations populations across Canada in a national dataset with more recent data. Methods: This study utilized the 2017 Aboriginal Peoples Survey, which includes data on the social and economic conditions of First Nations people living off reserve aged 15 years and over. Results: Results from a factorial ANOVA indicated that perceptions of income security, housing satisfaction, higher education, and employment are associated with increased self-reported mental health among First Nations individuals living off-reserve. Discussion: These results support the idea that individual mental health interventions on their own are not enough; instead, broader social interventions aimed at addressing inequities in various social determinants of health (e.g., housing first initiatives) are needed to better support individual wellbeing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".