Strategies to Diversify Canadian BSN Education: A Scoping Review
Bibliographic record
Abstract
Background: Indigenous people, Black people, and men have and continue to face exclusion from nursing education and the profession. Furthermore, persisting Eurocentric and heteronormative systems of oppression may exclude people who are Asian, Latin American, identify as LGBTQ2IA+, or have disabilities. We conduct this review to identify strategies implemented by Canadian baccalaureate nursing (BSN) programs to recruit and retain students from systemically and historically marginalized populations. Methods: Using JBI scoping review methodology, we conducted searches in the following datasets: CINAHL (EBSCO), MEDLINE (EBSCO), ProQuest Dissertations & Theses, Web of Science, and ERIC (EBSCO). A total of 1724 citations were identified. After title, abstract and full article screening, 35 articles were included for extraction. Findings: Of the 30 identified recruitment and retention strategies, pathway programs for Indigenous students are the most frequently cited strategy. Formal evaluation of all strategies are scarce, as are strategies designated specifically for the recruitment and retention of other non-Indigenous marginalized populations. Conclusion: Further research is needed to identify strategies in place today, followed by formal strategy evaluation. Formal evaluation of successful strategies may allow Canadian institutions across the country to adopt them into their BSN programs, thus manifesting diversity into Canadian BSN education and the nursing profession.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".