File of Uncertainties: Exploring student experience of applying decolonizing knowledge in practice
Bibliographic record
Abstract
It is critical that nursing education programs in Canada respond to the Truth and Reconciliation Commission’s call to develop a course about the documented impacts of Indigenous-specific racism on the health outcomes of Indigenous people. Initiatives such as San’yas Anti-racism Indigenous Cultural Safety Education, courses on trauma-informed care, and required Indigenous health and history classes in nursing programs are providing a solid beginning. However, the effectiveness of this education requires interrogation. This mixed methods study aimed to identify barriers and supports to incorporating decolonizing and antiracist knowledge into nursing practice with Indigenous patients/clients. Fourth-year nursing students at a Canadian university who had completed a core course on the impacts of colonization and Indigenous-specific racism on Indigenous health and wellness in Canada were surveyed to explore their experience of applying this knowledge during their clinical rotations. Sixteen participants responded to an anonymous online survey consisting of three short-answer open-ended questions and six Likert-style questions about their experiences. The emergent narrative themes and Likert-scale data indicate that although the students valued the information provided in the class, they continued to feel cultural tension and uncertainty when caring for Indigenous clients. Prominent areas of uncertainty included applying knowledge to practice, student confidence in disrupting racist treatment of Indigenous patients by their healthcare colleagues and knowing how to approach sensitive client situations to avoid re-traumatization. This article discusses the study’s implications and identifies areas for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".