Using Narrative Inquiry to Understand Anti-Muslim Racism in Canadian Nursing
Bibliographic record
Abstract
BACKGROUND: Islamophobia or, anti-Muslim racism, and more specifically, gendered islamophobia targeting Muslim women who wear a hijab is rising globally and is aggravated by the COVID-19 pandemic. However, anti-Muslim racism is not well understood in Canadian nursing. PURPOSE: This study utilized narrative inquiry to understand anti-Muslim racism through the experiences of nurses who wear a hijab with the goal of putting forward their counter-narrative that disrupts anti-Muslim racism in Canadian nursing. METHODS: Narrative inquiry informed by Critical Race Feminism, care ethics, and intersectionality were used to analyze the factors shaping anti-Muslim racism and composite narratives were used to present the results. RESULTS: The three composite narratives are: 'This is Who I Am: A Muslim Nurse with a Hijab and an Accent'; 'I Know What is at Play: Unveiling Operating Power Structures and Power Relations'; and 'Rewriting the Narrative: Navigating Power Structures and Power Relations'. These composite narratives constituted the nurses' counter-narrative. They revealed intersections of gendered, racial divisions of labour and religious narratives that shape anti-Muslim racism, as operating power relations in nursing, and how Muslim nurses reclaimed control to resist their racialized stereotypes. CONCLUSION: Findings suggest that anti-Muslim racism in nursing operates through multiple intersecting power relations. Using stories can mobilize transformational change so that anti-racist practices, policies, and pedagogy can be embraced.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".