Caring About Racism: Early Career Nurses' Experiences With Aboriginal Cultural Safety
Bibliographic record
Abstract
The Aboriginal population in Canada is the youngest and most rapidly growing demographic in the country, and more than half of the 1.4 million Aboriginal people in Canada live in urban centres (Aboriginal Affairs and Northern Development Canada, 2013). As a result, all nurses can expect to provide nursing care to Aboriginal peoples during the course of their work, regardless of the setting of their employment. Since the beginning of the new millennium, however, Canadian researchers have documented that Aboriginal people encounter racism and discrimination at the hands of health professionals when they access health care in this country. In an effort to address this racism and improve the experience of Aboriginal peoples in health care, the Canadian Association of Schools of Nursing (2013) now recommends that undergraduate nursing students receive cultural safety education during their nursing programs. This recommendation is significant because cultural safety differs in important ways from cultural competence, the dominant model of attending to cultural differences practiced by health professionals in North America. The purpose of this study was to examine the experiences of early career nurses translating knowledge of cultural safety into nursing practice. This qualitative research study used Adele Clarkeâ s (2005) Situational Analysis to interrogate the complex forces and colonial discourses that influence the practice of nurses with Aboriginal people in contemporary health care environments. Thirteen early career nurses and seven experienced northern nurses were interviewed as part of this study, and results showed that for these nurses, a culturally safe approach is one where respect and relationship are centred. The nursesâ narratives also revealed that health care professionals make use of discriminatory labels to withhold and delay care for Aboriginal patients, and that intervening on behalf of patients can provoke strong opposition from nursing colleagues in some settings. However, findings also suggest that cultural safety education can help early career nurses to resist and disrupt pervasive colonial discourses in the health care arena. Further, bearing witness to the suffering created by colonialism also informs the nursesâ motivation to work as allies with Aboriginal peoples, revealing the link between cultural safety and reconciliation.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".