Decentering Whiteness in Nursing Education: The Pitfalls, Tensions, and Opportunities
Bibliographic record
Abstract
Statement of Research Problem: The Truth and Reconciliation 94 Calls to Action (TRC) (2015) calls for the implementation of accessible and appropriate Indigenous health education. Schools of Nursing are increasingly implementing various approaches to teach Indigenous health. Limited research exists for Canadian nursing students' experiences of learning Indigenous health. This study explored undergraduate nursing students' experiences of learning Indigenous health from four Schools of Nursing across Canada. Particular attention is paid to the factors that shaped student and faculty experiences of learning and teaching Indigenous health, respectively, the facilitators and challenges, and what constitutes a safe and effective learning environment.\nMethods: Using a qualitative methodology informed by critical ethnography nine semi-structured interviews were conducted with six undergraduate nursing students and three faculty members. Postcolonial theory was used to inform the work and a thematic analysis was utilized to elicit themes.\nResults: Findings resulted in five overarching themes: (1) encountering racism in education and practice, (2) need for faculty development, (3) decentering whiteness in the classroom, (4) creating cultural safety in the classroom, and (5) from classroom to practice.\nConclusions: Findings suggest that for transformative work to take place, the education system needs to work towards better representation of Indigenous faculty and students. Healthcare and education settings need to collaborate to develop anti-racist, anti-oppression, and decolonizing policies and procedures to support the translation of classroom learning into clinical practice. All work should be in partnership with local Indigenous communities. The work remains ongoing and health care professionals are accountable to bettering and humanizing the landscape of care for all Indigenous peoples and persons of color. Education is a political act and one that requires more accomplices in dismantling the colonial structures of violence.
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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 | 0.107 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".