The Lived Experiences of Racism Among Indigenous Nursing Students Enrolled in a Canadian Baccalaureate Nursing Education Program
Bibliographic record
Abstract
Introduction: This study investigated whether Indigenous students in a school of nursing perceived, experienced, or observed instances of racism from instructors, fellow students, or patients during their nursing education program and/or clinical practice experiences, and the impact of such experiences. Background: Historically, nursing education has been deemed one of the more accessible post-secondary education programs for Indigenous people in Canada; however, certain challenges have become evident. These challenges continue to result in colonial practices, programs, and policies, both implicitly and explicitly, in nursing education today. While the number of Indigenous nursing students is increasing, the number of students who complete their programs of study is unknown. Method: We used a combination of semi-structured and narrative inquiry interviews with each participant to guide the interview process. We interviewed a convenience sample of 11 nursing students and analyzed data using content analysis. Findings: Nursing students in this study experienced issues with racist attitudes and stereotyping from non-Indigenous people. Both visible and non-visible Indigenous nursing students perceived and/or witnessed some form of racism from fellow students, instructors, patients, and other health care providers within the learning and hospital environments. Four predominant themes emerged from the interviews: cultural isolation, academic abilities, relationships, and resilience. Discussion: Over the past decade, nursing programs have been developed to improve the recruitment and retention of Indigenous nurses into the Canadian nursing workforce. Recognizing the impact of stereotypes on the academic performance of Indigenous nursing students is crucial for faculty, particularly in cases in which the program lacks culturally relevant role models. To address this, we make several recommendations. One limitation of this study is that sampling was sought only from one school of nursing. The other limitation was related to gender, as only one participant was male and 10 were female. Conclusion: In response to the experiences of racism reported by the participants, we strongly encourage schools of nursing to promote open and ongoing discussions about racism among students and faculty. We also recommend adopting strategies to establish safe, inclusive, and culturally welcoming learning environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".