Are sport and traditional Inuit games identified as tools in current Inuit suicide prevention strategies?: A content analysis
Bibliographic record
Abstract
Eekeeluak Avalak, an 18-year-old Inuk wrestler who won the first-ever gold medal for Nunavut at the Canada Summer Games in 2022, dedicated his win to his late brother who died by suicide in 2015. Avalak openly attributed sport - specifically wrestling - to saving his own life. This story raises important questions about the role of sport and traditional games in Inuit suicide prevention strategies. Few studies have examined the role of sport or traditional games in Inuit suicide prevention strategies. In an attempt to reduce Inuit suicide rates, in addition to the National Inuit Prevention Strategy, three of the four land claim regions that constitute Inuit Nunangat have suicide prevention strategies. In this study, we used settler colonial theory, critical Inuit studies, and content analysis to examine if and how sport and Inuit traditional games are identified as prevention tools in these Inuit suicide prevention strategies. The results demonstrate that sport and traditional games have largely been overlooked as protective factors in current Inuit-wide and land-claim specific suicide prevention strategies. Moving forward, evidence-based and community-driven approaches could be funded, created, implemented, and evaluated as culturally-safe Inuit mental health intervention models to address the disproportionately high suicide rates among Inuit in Inuit Nunangat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".