Exploring Cultural Healing and Mental Wellness in a Northern Saskatchewan First Nations Community
Bibliographic record
Abstract
Several health inequities exist between the Indigenous and non-Indigenous populations in Canada. These disparities are a result of colonization, which aimed to disconnect Indigenous Peoples from their land, language, and connection to community. This forced assimilation severed connection between Indigenous Peoples and traditional methods of promoting wellness. Connecting to culture appears to play an important role in enhancing mental wellness among Indigenous individuals and communities.\nThe aims of this project were to: a) explore the role that culture has in promoting mental wellness for First Nations individuals from the Lac La Ronge Indian Band (LLRIB), b) empower young adults from the LLRIB to share their stories of connecting to culture and the impact that this has on their lives.\nThis community-based project was conducted in collaboration a Community Advisory Committee. Using purposive and subsequent snowball sampling, 5 participants between ages 18 and 25 from the LLRIB were recruited to partake in a two-part photovoice project. First, participants captured photographs representing their experiences with connecting to culture and the role that this has in improving their mental wellness. Following this, the participants attended a one-on-one discussion with the student researcher where they shared the stories behind the photographs they presented. Data were analyzed using narrative analysis.\nNarratives were arranged into one of four overarching categories based on the First Nation Mental Wellness Continuum Framework: hope, belonging, purpose, and meaning. Taking a strengths-based approach, the positive influence of culture and community on individual wellbeing is evident and we gain an understanding of how connecting to culture acts as a protective mechanism when addressing suicide prevention.\nImplications of findings contribute to a greater understanding of the role that connecting to culture has in improving the wellness of Indigenous Peoples. The results of this project could guide future research endeavours with Indigenous communities to explore wellness of other communities. Overall, the project improves understanding of the idea that Indigenous Peoples find strength and wellness within their culture from connection to community, land, and language.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".