Engaging Poo’miikapii & Niitsitapiisinni: The Development & Implementation of Community-Based Graduate Programs to Support Community Wellness
Bibliographic record
Abstract
This thesis investigates how community-based graduate programs in local Indigenous approaches to wellness can be most effectively developed and implemented. The Poo’miikapii: Niitsitapii Approaches to Wellness, and Niitsitapiisinni: Real Peoples’ Way of Life programs at the University of Calgary were used as examples to demonstrate this. Ten storytellers engaged in research conversations to share their feedback and experiences regarding the development and implementation of the Poo’miikapii and Niitsitapiisinni programs. Research conversations and course outlines were analyzed using Archibald’s (2008) storywork analysis. Themes of relationship building and maintenance, Elder engagement, community-based Indigenous pedagogy and curriculum, and decolonizing and Indigenizing the academia were identified. A framework for universities, organizations, and communities to implement similar programs is discussed. Considerations of how to collaboratively develop and implement on reserve, community-based wellness programs with an emphasis on experiential, land-based, and Elder-guided learning are included. Given the vast diversity among Indigenous communities, this framework should be interpreted as a flexible guideline that can be altered to align with Indigenous communities’ unique practices.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".