Artificial Intelligence A Tool in Suicide Prevention Amongst Inuits of Canada: Systematic Review
Bibliographic record
Abstract
The Inuits of Canada who live in the Nunavut territory are confronted by the problem of a higher suicide rate than the rest of Canada and the world. The suicide rate in the region is 10 times higher for the general population and 25 times higher among men than in the rest of Canada. The problem is partly linked to mental health issues, yet because the region is remote and isolated, Inuits do not have adequate access to culturally competent mental health support and resources. Consequently, in empowering the community to deal with mental health issues, for suicide prevention, this systematic review involving a comprehensive review and analysis of eight papers published between December 2014 and December 2024 justifies the suitability of Artificial Intelligence (AI) tools for suicide prevention. Additionally, ethical risks should be identified and minimized, stakeholders actively involved, and AI algorithms consistently trained to increase accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".