Organizational Initiatives for the Recruitment, Retention and Advancement of Black Nurses: A Rapid Review
Bibliographic record
Abstract
Introduction: Black nurses are under-represented in the Canadian nursing workforce. A legacy of discrimination and systemic barriers reinforce the under-representation of Black nurses in the nursing workforce throughout the health system. Objective: The objective of this study was to identify and describe organizational initiatives for the recruitment, retention and advancement of Black nurses in the healthcare system. Methods: We conducted a rapid review of peer-reviewed and grey literature regarding the recruitment, retention and advancement initiatives for Black nurses, in North America and the UK. Results: Thirty-eight sources were included in this review. Majority of the included sources focused on leadership initiatives that described a multi-pronged approach for recruitment, retention or advancement. Examples of useful initiatives included mentorship, dedicated leadership and advancement programs, as well as supportive institutional policies. In addition, several Black nurse-led organizations/initiatives were identified. Conclusion: These findings highlight the importance of multi-pronged approaches to enhance and support the Black nurse workforce. In addition, implementing and evaluating initiatives is critical to understanding how workforce representation and inclusion is strengthened.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".