Navigating the Intersection of Race, Gender, and Nursing: Voices of Black Canadian Male Nurses
Bibliographic record
Abstract
Studies into the experiences of Black nurses in Canada's healthcare system provide policymakers with a deeper understanding into designing policies and practice guidelines to best support equity and diversity. With male nurses making up 9% of the nursing population in Canada, there remains a paucity of studies into their experiences and significantly less for Black male nurses (BMNs). The World Health Organization's call for more nurses means an increase in Internationally Educated and Canadian-born BMNs who will experience sociocultural stereotypes and biases that underpin nursing practices. BMNs are left to navigate intersections of race and gender power relations within nursing. Remaining invisible and voiceless within nursing literature, and discriminated against in the workplace culture, this study uses an exploratory qualitative approach to understand the experiences of six BMNs working in the Greater Toronto Area and the strategies they use to navigate the intersections of race and gender that sustain the negative stereotypes and tropes of the Black man. The findings disclose the need for policymakers, nursing administrators, and organizations to co-create policies that support dynamically tailored mentorship programs and practice guidelines when dismantling anti-Black racism and promote inclusivity and a sense of belonging.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.058 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".