Creating a Future of Our Own Design: The International Indigenous HealthFusion Team Challenge as a Promising Practice to Support Indigenous Students in Health Fields
Bibliographic record
Abstract
Training and recruitment of First Nations and Indigenous health professionals is part of reconciliation, addressing health disparities and embedding cultural safety and humility into the health ecosystem of the province of British Columbia (BC), Canada. Calls to develop the First Nations and Indigenous health workforce are articulated within the Truth and Reconciliation Commission of Canada’s Call to Action 23, BC’s Transformative Change Accord: First Nations Health Plan, and the seven directives that guide the work of the First Nations Health Authority in BC and its health governance partners. This article brings forward the voices of current Indigenous students training in allied health professions at the University of British Columbia and their Indigenous mentors who participated in the 2018 International Indigenous HealthFusion Team Challenge in Sydney, Australia. The Challenge represents a promising practice in training Indigenous health professionals here in BC as it: (1) Affirmed their Indigenous identity, knowledge, and aspirations, supporting them to become more “visible” as Indigenous students; (2) Created a space where both Indigenous and mainstream health discipline knowledges were encouraged, valued, and respected; (3) Provided opportunity to connect with Indigenous peers and health leaders; and (4) Built students’ confidence to take on leadership roles. First Nations and Indigenous students studying in health fields represent the future of BC’s health and wellness ecosystem that brings together the best of Indigenous and mainstream healing approaches. Creating opportunities for students to grow as Indigenous health leaders is part of reconciliation and the new relationship represented by the BC First Nations Health Governance Structure.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.035 | 0.022 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".