Exploring wellness and Wiidooktaadyang (we are helping one another) in Nipissing First Nation
Bibliographic record
Abstract
Purpose First Nations peoples have a long history of what it means to be well, but this perspective has not shaped their health and wellness experiences in Canadian healthcare systems. In response to calls for First Nation self-determination in health, this study provided one First Nation community with the opportunity to articulate what it means to be well. Wiidooktaadyang , meaning ‘we are helping each other', describes a Nipissing First Nation (NFN) philosophy and a relational approach to realising wellness. From this relational approach, this research explored: 1) how NFN debendaagziwaad (NFN members) on reserve, off reserve and staff understood and experienced wellness; 2) what NFN debendaagziwaad perceived as the ‘appropriate kind of help' to facilitate wellness; and 3) how understandings of wellness and helping can inform a community-owned service delivery model. Methods Designed with an NFN advisory committee, this study employed a qualitative design using a Two-Eyed Seeing theoretical and methodological approach, which paired participatory action research with Indigenous research methods. Ninety participants, grouped according to on reserve, off reserve or staff members, engaged in conversational interviews with community-based research assistants. Their stories were analysed using thematic analysis adhering to Indigenous research principles. Main findings The story of wellness of NFN debendaagziwaad comprised five themes: 1) Connectedness, 2) Living the Medicine Wheel, 3) Belonging, 4) Experiencing colonialism and 5) Reclaiming NFN ways. Graphic artists facilitated a process of graphical analysis to illustrate themes as conceptual models. This project moved knowledge into meaningful action by meeting with NFN leaders to describe how they could apply the wellness models to their service integration model. Principal conclusions This paper contributes to new knowledge by documenting NFN members' stories of what it means to be well and their recommendations for reclaiming wellness. The knowledge helped to inform community service planning in a First Nations community. Particularly critical is that the models acknowledge First Nation peoples' power to determine their health and wellness experiences.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".