Exploring South Asian women’s perspectives and experiences of maternity care services: A qualitative evidence synthesis
Bibliographic record
Abstract
BACKGROUND: The United Nations Women and other sources have highlighted the poor maternal and neonatal care experienced by South Asian women, emphasizing the need to understand the cultural factors and specific experiences that influence their health-seeking behavior. This understanding is crucial for achieving health equity and improving health outcomes for women and infants. OBJECTIVES: This study aims to examine and synthesize qualitative evidence on the perspectives and experiences of South Asian women regarding maternity care services in destination countries. METHODS: A systematic review was conducted using the Joanna Briggs Institute's approach. Eight databases were searched for studies capturing the qualitative views and experiences of South Asian women - Medline, EMBASE, CINAHL Plus, Global Health, Scopus, PsycInfo, British Nursing Index and the Applied Social Science Index and Abstracts. Qualitative and mixed method studies written in English are included. The methodological quality of the included studies was assessed using the JBI's QARI checklist for qualitative studies and the MMAT checklist for mixed-methods studies. RESULTS: Fourteen studies, including twelve qualitative and two mixed-methods studies, were identified and found to be of high methodological quality. The overarching theme that emerged was "navigating cross-cultural maternity care experiences." This theme encapsulates the challenges and complexities faced by South Asian women in destination countries, including ethnocultural and religious differences, communication and language barriers, understanding different medical systems, and the impact of migration on their maternity care experiences. CONCLUSIONS: South Asian migrant women often have expectations that differ from the services provided in destination countries, leading to challenges in their social relationships. Communication and language barriers pose additional obstacles that can be addressed through strategies promoting better communication and culturally sensitive care. To enhance the utilization of maternity healthcare services, it is important to address these factors and provide personalized, culturally sensitive care for South Asian migrant women.
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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 | 0.038 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".