Culturally competent care across borders: Implementing culturally responsive teaching for nurses in diverse workforces
Bibliographic record
Abstract
The migration of healthcare professionals, including nurses, is a global phenomenon. It is driven by various factors, including the pursuit of better opportunities, living conditions, and professional development, as well as political instability or conflict in their home countries. The World Health Organization (WHO) has noted that high-income countries often rely on foreign-trained nurses to fill gaps in their healthcare systems [1]. For instance, as of 2021, over 40% (52 million) of all nurses in the United States (US) were expatriates [2]. In the United Kingdom (UK), the percentage of expatriate nurses was even higher, reaching approximately 18% in 2021 [3]. Owing to globalization and migration, healthcare providers must possess cultural competence to deliver improved care [4,5]. Culturally responsive teaching (CRT) is rooted in the idea that culture plays a vital role in shaping people's behaviors, beliefs, values, and communication styles [6]. Furthermore, these cultural factors influence patients' perspectives on health, illness, healing, and their preferences for care and communication [7]. By recognizing and embracing these cultural differences, nurses can provide more effective and compassionate care to their diverse patient population [8]. This paper explores the significance of CRT for nurses in diverse multinational workforces and provides examples from various countries, such as the US, Canada, Australia, the UK, Qatar, and Singapore. The previously mentioned countries represent diverse geographical regions and contain multicultural societies. They are representative examples of places where healthcare systems must adapt and cater to a culturally diverse population.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".