A Nurse's Journey with Cultural Humility: Acknowledging Personal and Professional Unintentional Indigenous-specific Racism
Bibliographic record
Abstract
This is a first-person reflection of my journey through cultural humility to identify a connection between my inherent beliefs about Indigenous Peoples and Indigenous-specific systemic racism. The co-authors of this paper provided guidance, mentorship, and support in organizing the framework due to the challenging and sensitive nature of the content. As part of my relational practice, I worked with a Cree scholar to write this paper. As a descendant of white European colonial settlers, I grew up in a small community in Western Canada populated by people of similar backgrounds. My exposure to Indigenous Peoples and culture was very minimal; however, conversations and attitudes about Indigenous Peoples generally centered around negative and racist stereotypes. Childhood games and jokes insidiously contributed to the construction of my worldview by dehumanizing, belittling, and humiliating Indigenous Peoples. A necessary part of my journey was to recognize how these words and attitudes have informed my worldview and at the same time hurt Indigenous Peoples. Historical facts of Indigenous treatment were brushed off or minimized as something that happened in the past. Although sharing my experience is uncomfortable, I am compelled to identify and acknowledge how the deep-rooted beliefs and attitudes that I have towards Indigenous Peoples have been shaped by my education, culture, and experiences. I hope that my own developing journey with cultural humility may serve as a guide to deconstructing the historical, personal, and professional ways in which Indigenous-specific racism exists and is perpetuated in health care. My own first steps are an open invitation for the nursing profession to similarly begin to address unintentional and intentional racism in healthcare. By understanding Canadian history, committing to allyship, advocating for social justice, actively intervening by speaking up, and integrating trauma-informed care/principles into our practice, we may begin to effectively address Indigenous-specific racism in health care.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".