From head to heart- listening to Manitoba healthcare leaders' experience of structural barriers with meaningful implementation of Canada's Truth and Reconciliation Commission's Calls to Action
Bibliographic record
Abstract
As a Winnipeg Métis woman and practicing Registered Nurse, health leader, and traditional ceremony person- reconciliation in Canada’s healthcare sector is of paramount personal and professional importance for Principle Investigator Kee-Knee Konee O-Chay-Ches-Ki Sew. Settler-colonial relations, which permeate every sector in Canada, continue to pose multiple threats to Indigenous sovereignty. One reason for this is that current structures intentionally silence Indigenous ontology. The purpose of this study is to move discussions about Indigenous health away from a health disparities’ focus and towards one that centers Indigenous ways of knowing, being and questioning. At its core, this study explores the present-day effects of colonialism influencing healthcare leaders within Manitoba. Methods: Utilizing the Medicine Wheel as our framework, this study employs an Indigenous ontology, which acknowledges relationality as central to its methodology. One-on-one interviews followed by a sharing circle, this qualitative study explores the emotional and social impacts of colonization experienced by of healthcare policy makers and administrators working in Manitoba. Eight storytellers shared their perceptions and experiences of promoting meaningful reconciliatory practices, either through policy or activities in the workplace. Results: We found that the concepts of voice; claiming; dismantling systemic oppression; and roles and life calling intersect at the center of the Medicine Wheel to support balanced and meaningful engagement with the TRC (2015) Calls to Action. Significance: Ultimately, reconciliation must begin by Canada acknowledging the right of Indigenous Persons and their ancestral rights to self-determinations, while simultaneously understanding that the current relationship between existing colonial structures and Indigenous people requires major restructuring to effectively address the health needs of all Canadians. By privileging relationality and other Indigenous epistemologies, this study allows for an experiential understanding, reflexivity, and insights on how to move forward with reconciliatory 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".