Protective Factors in the Inuit Population of Nunavut: A Comparative Study of People Who Died by Suicide, People Who Attempted Suicide, and People Who Never Attempted Suicide
Bibliographic record
Abstract
Epidemiological data shows an alarming prevalence of suicide in Aboriginal populations around the world. In Canada, the highest rates are found in Inuit communities. In this article, we present the findings of a secondary analysis conducted with data previously collected as part of a larger study of psychological autopsies conducted in Nunavut, Canada. The objective of this secondary analysis was to identify protective factors in the Inuit population of Nunavut by comparing people who died by suicide, people from the general population who attempted suicide, and people from the general population who never attempted suicide. This case-control study included 90 participants, with 30 participants in each group who were paired by birth date, sex, and community. Content analysis was first conducted on the clinical vignettes from the initial study in order to codify the presence of protective variables. Then, inferential analyses were conducted to highlight differences between each group in regards to protection. Findings demonstrated that (a) people with no suicide attempt have more protective variables throughout their lifespan than people who died by suicide and those with suicide attempts within the environmental, social, and individual dimensions; (b) people with suicide attempts significantly differ from the two other groups in regards to the use of services; and (c) protective factors that stem from the environmental dimension show the greatest difference between the three groups, being significantly more present in the group with no suicide attempt. Considering these findings, interventions could focus on enhancing environmental stability in Inuit communities as a suicide prevention strategy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".