Building a Global Indigenous Health Community of Practice
Bibliographic record
Abstract
This presentation provided an overview of our experience building an Indigenous Health Community of Practice (IH-CoP) at Mount Royal University. Our Faculty of Health and Community Studies has a variety of academic departments, including Nursing, Social Work, Justice, and Physical Education that work in collaboration with The Iniskim Centre, our support system for First Nations, Inuit and Metis students. In 2010, we recognized the need for a coordinated forum to learn from each other, and develop scholarship, community service, and student learning opportunities to ensure that vital, sensitive work was not done in isolation. We referred to the World Health Organization (2007) to provide foundational understanding about indigenous health, and defined our IH-CoP domain (membership), community (interactions), and practice (our collective work) based on Wenger's (2006) theory. Our approach is informed by the Royal Commission on Aboriginal Peoples (1996) to make certain that our interactions are based on “mutual recognition, respect, sharing and responsibility”. Over the years, our IH-CoP membership has grown, and our local group has emerging global connections. We are actively exploring strategies to foster these connections in order to promote indigenous health as a shared global priority. Examples of recent Canadian and Hawaiian collaboration illustrated how links have been created thus far, including a research proposal to develop a global IH-CoP network. We hope that presenting our work will help us move forward together to promote global indigenous health. Moreover, our IH-CoP is cornerstone to a broader vision to create a Human Dignity Commons.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.043 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".