African Canadian nurses in the nursing profession in Canada: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to synthesize the evidence on African Canadian nurses in the nursing profession in Canada. INTRODUCTION: With approximately 1.2 million people of African descent, Canada has committed to addressing the United Nations' decade for people of African descent. Intergenerational racism continues to result in multisectoral discrimination against African Canadians. Studies suggest that African Canadians are under-represented in nursing, and encountering systemic barriers to entering and advancing in the profession. Additionally, African Canadian nurses experience racism from patients and colleagues, as well as systemic racism through hiring and promotion. INCLUSION CRITERIA: This review will consider sources that include African Canadian nurses who identify as Black or as of African descent. All levels of professional nursing practice will be included (practical nurses, registered nurses, and advanced practice nurses, including nurse practitioners and clinical nurse specialists). Qualitative, quantitative, and mixed methods studies and gray literature will be searched. METHODS: This review will be conducted in accordance with the JBI methodology. Databases to be searched from inception to the present include CINAHL, MEDLINE, Embase, Sociological Abstracts, Gender Studies Database, America: History and Life, PsycINFO, Academic Search Premier, and Scopus. Studies published in English and French will be included. A comprehensive search strategy developed with a librarian will be used to retrieve relevant sources. Two independent screeners will screen titles and abstracts as well as full texts of relevant sources. Data will be extracted by two independent extractors then presented narratively, using appropriate tables and figures. SYSTEMATIC REVIEW REGISTRATION NUMBER: Open Science Framework Preregistration October 3, 2019. Open Science Framework Link for Abstract https://osf.io/6a2fe/?view_only=57d86d5b7c1d464182692d0f4bb9b396.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".