Development of an educational resource: cultural safety with patients who identify as Black, African Nova Scotian, African or Caribbean descent
Bibliographic record
Abstract
Background: Black patients have faced health disparities and mistreatment in healthcare settings (Public Health Agency of Canada, 2020). The United Nations (UN) declared a decade for people of African descent from 2015-2024 (UN, n.d.). The UN called for recognizing this distinct population and protecting and promoting their human rights (UN, n.d.). Racial discrimination and microaggressions contribute to current and historical mistrust in the healthcare system (CDC, 2020; Cénat et al., 2022a; Cénat et al., 2022b; Waldron et al., 2023; Wolinetz & Collins, 2020). To improve the health experiences of Black patients, healthcare staff must create culturally safe environments. Methods: I conducted a literature review, consultations, and an environmental scan to explore and analyze the healthcare experiences of Black patients and the experiences of healthcare staff working with Black patients. Through the literature review, I focused on global experiences while I explored the local context through the environmental scan and consultations. Results: Black patients experienced racial discrimination, microaggressions, and a lack of trust in the health system (CDC, 2020; Cénat et al., 2022a; Cénat et al., 2022b; Waldron et al., 2023; Wolinetz & Collins, 2020). The mental health impacts of these experiences included anxiety, depressive symptoms, sleep problems, and decreased help-seeking (Cénat et al., 2022a; Cénat et al., 2022b; Chan et al., 2023; Moody et al., 2022; Nguyen et al., 2023; Waldron et al., 2023; Washington & Randall, 2023). Cultural safety training can increase healthcare providers' knowledge and improve patient interactions (Browne et al., 2021; Kaihlenan et al., 2019; Pimental et al., 2022; Yaphe et al., 2019). Based on these results, I created an educational resource, including a PowerPoint presentation and teaching guide for a one-day course in cultural safety with Black patients.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".