Organizational and Institutional Initiatives for the Recruitment, Retention, and Advancement of Black Nurses in the Canadian Healthcare System: A Rapid Review Protocol
Bibliographic record
Abstract
Introduction: In response to protests for racial justice, several organizations and institutions have made public declarations denouncing anti-Black racism. One prominent sector emphasizing their commitment to addressing anti-Black racism is health care—more specifically, nursing. To address anti-Black racism, many Canadian organizations and institutions have announced initiatives to recruit, retain, and support the advancement of Black nurses. Our team is interested in charting these initiatives to inform future policy and program decisions related to the recruitment, retention, and advancement of Black nurses. Objective: The objective of this review is to identify and chart evidence of organizational and institutional initiatives related to the recruitment, retention, and advancement of Black nurses in Canada. Inclusion criteria: This rapid review will include sources focused on Black nurses in Canada. Further, this review is focused on the organizational or institutional initiatives that support or facilitate aspects of recruitment, retention, or advancement of Black nurses in the workforce in Canada. Methods: A comprehensive search, developed in collaboration with a library scientist, will be used to locate peer-reviewed and grey literature from select databases and repositories. Databases will be searched from time of inception, and language will be restricted to English and French sources. Title and abstract screening as well as full-text review will each be completed by two independent reviewers. Sources will be included if they meet the inclusion criteria for the population, concept, and context. Data will be extracted by two reviewers using an extraction tool. Data will be reviewed and consolidated before being presented narratively and visually.Protocol Registration: The protocol has been registered with Open Science Framework (OSF) on March 1st, 2023.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".