Black nurses in the nursing profession in Canada: a scoping review
Bibliographic record
Abstract
Background With migration occurring over a series of centuries, dating back to the 1600’s, the circumstance regarding Black people in Canada is a complex account. A plethora of social issues and the failure to adequately acknowledge and reconcile historical issues, has resulted in health inequity, disparities and knowledge gaps, related to the Black population in Canada. In nursing, historical records indicate a legacy of discrimination that continues to impact Black nurses. The profession has begun reckoning with anti-Black racism and the residual effects. This scoping review sought to chart the existing evidence on Black nurses in the nursing profession in Canada. Methods JBI methodology was used to search peer-reviewed evidence and unpublished gray literature. Sources were considered for inclusion based on criteria outlined in an a priori protocol focusing on: 1) Canada 2) Black nurses and 3) nursing practice. No restrictions were placed on date of publication and language was limited to English and French. All screening and extractions were completed by two independent reviewers. Results The database search yielded 688 records. After removing duplicates, 600 titles and abstracts were screened for eligibility and 127 advanced to full-text screening. Eighty-two full-text articles were excluded, for a total of 44 sources meeting the inclusion criteria. Seven sources were identified through gray literature search. Subsequently, 31 sources underwent data extraction. Of the 31 sources, 18 are research (n = 18), six are commentaries (n = 6); one report (n = 1) and six are classified as announcements, memoranda or policy statements (n = 6). The review findings are categorized into five conceptual categories: racism (n = 12); historical situatedness (n = 2); leadership and career progression (n = 7); immigration (n = 4); and diversity in the workforce (n = 4). Conclusions This review reveals the interconnectedness of the five conceptual categories. Racism was a prominent issue woven throughout the majority of the sources. Additionally, this review captures how racism is exacerbated by intersectional factors such as gender, class and nationality. The findings herein offer insight regarding anti-Black racism and discrimination in nursing as well as suggestions for future research including the use of diverse methodologies in different jurisdictions across the country. Lastly, the implications extend to the nursing workforce in relation to enhancing diversity and addressing the ongoing nursing shortage.
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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 | 0.026 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.039 | 0.058 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".