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 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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".