Person-centred care for migrants: a narrative review of healthcare literature.
Bibliographic record
Abstract
According to the World Migration Report, the number of international migrants has steadily increased in the past 50 years. This has led to an increasing need for healthcare to incorporate a variety of perspectives for migrants. However, healthcare systems still show gaps in accommodating diverse cultural perspectives. Given the increasing attention to person-centred care, there is both an opportunity and a need to explicate how person-centred care (PCC) can help to improve healthcare for migrants. Therefore, we conducted a narrative literature review on cultural dimensions of PCC practice for migrants. A scoping review by Forsgren et al. (2025) identified 1,351 articles from a search of PubMed, Scopus, PsycINFO, CINAHL, and Web of Science databases. From these, nine studies that met the following inclusion criteria were selected: (1) about cultural dimensions of health care for migrants (immigrants and refugees), (2) in any health care settings, (3) written in English, and (4) published within the last 10 years (January 1, 2023-December 31, 2023). The studies included participants from diverse ethnicities, racial backgrounds, and countries of origin. Seven studies were undertaken in primary care, long-term care, or outpatient clinics; one study was on health education; and one additional study focused on the acute care environment. The review led to three main practices: (a) enhancing migrants' ability to participate in their healthcare, (b) building intercultural partnerships, and (c) promoting cultural education of healthcare providers. These practices underscore the significance of respecting diverse cultural beliefs about shared decision-making and understanding how PCC practice is perceived in different cultural contexts. The results also indicate a need for educational programs that equip healthcare providers with intercultural communication skills and knowledge to provide culturally sensitive PCC. Overall, this study highlights the importance of integrating PCC with interculturalism as a way to foster a more nuanced and responsive understanding of the cultural dimension of care.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".