The Interplay of Perceived Mental Health and Suicidal Ideation among Aboriginal Peoples in Canada: A Secondary Analysis of Aboriginal Peoples Survey
Bibliographic record
Abstract
Abstract The disproportionately high suicide rates among Aboriginal populations in Canada, linked to historical trauma and systemic inequities, necessitate targeted mental health interventions. This study investigated the relationship between perceived mental health and suicidal ideation among the Aboriginal population in Canada, First Nations, Inuit, and Métis, using data from the 2012 Aboriginal Peoples Survey (APS) (n=4,725). The analysis employed chi-square tests and bivariate logistic regression to examine the association between self-perceived mental health, suicidal ideation, and socio-demographic factors. Results revealed significant associations between suicidal ideation, age group (p<.001), and sex (p<.001), with younger individuals (18-24 years) and females reporting a higher prevalence of suicidal thoughts. Also, bivariate logistic regression showed that being between the ages of 25 and 34 was a strong predictor of suicidal thoughts (OR = 1.954, p =.000), while being female (OR =.648, p =.000) and having a higher socioeconomic status (OR =.678, p =.000) were weaker predictors. Although Aboriginal identity was not significantly associated with suicidal ideation, self-perceived mental health was. These results made it clear that focused and targeted mental health interventions and culturally appropriate suicide prevention strategies are needed right away in Aboriginal communities in Canada. These strategies need to meet the needs of vulnerable groups, like younger people and women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".