Strategies to diversify Canadian baccalaureate nursing education: A scoping review protocol
Bibliographic record
Abstract
Objective: This review seeks to identify strategies that have been implemented by Canadian baccalaureate nursing (BSN) programs to recruit and retain students of systemically and historically marginalized populations. Introduction: Diversity within the nursing workforce has been shown to improve patients’ healthcare experiences. However, Eurocentric-heteronormative Canadian society has facilitated the exclusion of people who are Indigenous, Black, Asian, LGBTQ2IA+, have disabilities, or identify as male from nursing education. The country’s first Indigenous nurse, Edith Monture, graduated in 1914 from a program in the United States after being denied access to training in Canada. This story repeats approximately 30 years later when Bernice Redmon graduated from an American training program before returning to Canada to become the country’s first Black nurse. Implementing strategies to recruit and retain students from these historically underrepresented groups could diversify the nursing workforce; thus, improving patient experiences with healthcare. Inclusion criteria: This review will include English language publications dating from 1990 that describe recruitment and/or retention strategies aimed to increase and/or sustain the enrolment of students of systemically and historically marginalized populations within Canadian baccalaureate nursing education programs. Methods: Using JBI scoping review methodology, sources will be searched in the following datasets: CINAHL (EBSCO), MEDLINE (EBSCO), ProQuest Dissertations & Theses, Web of Science, and ERIC (EBSCO) to identify strategies implemented within Canadian BSN programs. The search will be limited to publications dated from 1990 and will include terms to focus on Canadian content. To capture grey literature, websites of approved Canadian BSN programs will be hand searched for recruitment and/or retention strategies.
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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 | 0.089 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.036 | 0.027 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".