Understanding the invisibility of black nurse leaders using a black feminist poststructuralist framework
Bibliographic record
Abstract
AIM: This paper explores the invisibility and underrepresentation of Black nurses in formal and informal leadership roles using a Black feminist poststructuralist framework. The paper describes historical and contemporary challenges experienced by Black nurses throughout their nursing education and in practice. It also highlights how social and institutional discourses continue to marginalise and oppress Black nurses as leaders and render them invisible. BACKGROUND: Diversity among nursing leaders is essential to inform health care delivery, develop inclusive practices and provide culturally sensitive care. Despite this glaring need for diversity within nursing in Canada, there remains a significant underrepresentation of Black nurses in the workforce and as leaders. DESIGN: This is a discursive paper on Black nurses in nursing education and the workforce as well as their location as leaders in health care through a critical analysis using Black feminist poststructuralism. METHODS: A review of the literature involved searching electronic databases CINAHL, NovaNet, PubMed and Google Scholar using keywords including: Black; African; Nurses; Leaders; Feminism; Poststructural. Articles were screened by titles and abstracts before accessing full-text for relevant articles. RESULTS: Black feminist poststructuralism uncovers how power, language, subjectivity and agency are constructed by the historically ingrained social and institutional discourses of everyday life for Black nurses. Experiences of discrimination and oppression were common throughout nursing education and practice for Black nurses, resulting in feelings of marginalisation and isolation. CONCLUSION: The invisibility of Black nurse leaders is the result of generational oppression and discrimination manifested through discourses. Systemic, institutional and historical discourses perpetuate barriers for Black nurse leaders, resulting in their invisibility or absence in practice. RELEVANCE TO CLINICAL PRACTICE: This paper is designed to generate discussion related to the invisibility of Black nurse leaders by providing an understanding of the historical experiences of Black people, their entry into the nursing profession and the present day challenges they face. This discussion will inform health care practice, policy, and structuring by identifying the barriers to leadership for Black nurses.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| 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".