Understanding Race and Racism in Nursing: Insights from Aboriginal Nurses
Bibliographic record
Abstract
Purpose. Indigenous Peoples are underrepresented in the health professions. This paper examines indigenous identity and the quality and nature of nursing work-life. The knowledge generated should enhance strategies to increase representation of indigenous peoples in nursing to reduce health inequities. Design. Community-based participatory research employing Grounded Theory as the method was the design for this study. Theoretical sampling and constant comparison guided the data collection and analysis, and a number of validation strategies including member checks were employed to ensure rigor of the research process. Sample. Twenty-two Aboriginal nurses in Atlantic Canada. Findings. Six major themes emerged from the study: Cultural Context of Work-life, Becoming a Nurse, Navigating Nursing, Race Racism and Nursing, Socio-Political Context of Aboriginal Nursing, and Way Forward. Race and racism in nursing and related subthemes are the focus of this paper. Implications. The experiences of Aboriginal nurses as described in this paper illuminate the need to understand the interplay of race and racism in the health care system. Our paper concludes with Aboriginal nurses' suggestions for systemic change at various levels.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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 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".