Factors associated with suicidal ideation for First Nations, Inuit, and Métis peoples using the 2017 Aboriginal Peoples Survey
Bibliographic record
Abstract
Suicide is a global problem that results in a significant number of deaths and disabilities every year. In Canada, approximately 4,500 people die by suicide annually. Indigenous peoples are at an increased risk for suicide, and First Nations and Métis adults experience twice as many suicides as non-Indigenous peoples. The rate of suicide is even higher for Inuit adults, at four times that of non-Indigenous peoples. This project utilized the 2017 Aboriginal Peoples Survey (APS) with a sample of (N = 20,660) to examine unique protective and risk factors associated with suicidal ideation among Canada’s First Nations, Inuit, and Métis peoples. The prevalence rate of Indigenous respondents who experienced suicidal ideation during the lifetime and last 12 months was found to be 18.9% and 5.6%, respectively. Three protective factors (language, cultural activities, sense of belonging) and nine risk factors (alcohol, drugs, mental health, mood disorders, anxiety disorders, health status, income, housing, and residential school attendance) were analyzed using various statistical tests, including Chi-squared analyses, logistic regression, and multiple logistic regression on the outcome variable of suicidal ideation during the lifetime and last 12 months. Findings revealed only partial support for the hypothesized protective factors and overwhelming support for risk factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".