Challenges and strategies in accessing perinatal care for refugee pregnant women: scoping review
Notice bibliographique
Résumé
The perinatal period, spanning from the 22nd week of gestation to seven days after birth, requires comprehensive, continuous, and high-quality care, which is essential for preventing complications and fostering strong bonds between healthcare providers and patients. However, refugee women—a population that, as of 2024, includes over 122 million forcibly displaced individuals—face heightened risks during pregnancy due to structural, cultural, and linguistic barriers, as well as discrimination and unfamiliarity with the healthcare systems of host countries. This study aimed to analyze, through a scoping review, the barriers and facilitators in accessing perinatal care for refugee pregnant women, identifying key challenges, strategies for care provision, and gaps in the scientific literature. The methodology followed the Joanna Briggs Institute (JBI) guidelines, using the PCC mnemonic (Population: refugee women in the perinatal period; Concept: structure and approach of perinatal care; Context: access to care in host countries). Searches were conducted in PubMed, ScienceDirect, BVS, ERIC, and LILACS using standardized descriptors, with no time or geographic restrictions, and included literature in Portuguese, English, and Spanish. Out of 1,107 identified records, after removing duplicates and applying eligibility criteria, 43 studies were included in the review. The results revealed that since 2007, scientific production on the topic has increased, initially highlighting structural and sociocultural challenges in caring for refugee pregnant women, such as dependence on family authorization to access services, language barriers, and lack of care continuity. From 2015 onward, evidence grew regarding late initiation of prenatal care, dissatisfaction with care quality, and higher rates of adverse outcomes, including preterm births, low birth weight, and obstetric complications. Although multidisciplinary and culturally sensitive care models have shown positive impacts on patient satisfaction and adherence, many refugee women still experience insecurity, lack of privacy, prejudice, and insufficient psychosocial support. Most studies employed quantitative methods, particularly cohort designs comparing perinatal outcomes between refugees and local populations, revealing significant disparities. Research was predominantly conducted in high-income countries—such as Australia, Turkey, the Netherlands, Germany, and Canada—reflecting contexts with higher migration flows, while low- and middle-income countries, despite hosting highly vulnerable populations, were underrepresented. The discussion highlighted that challenges in accessing perinatal care include delayed prenatal care initiation, fewer consultations, lack of providers trained in culturally sensitive practices, discrimination, language barriers, and limited awareness of available rights and resources. Additional factors such as frequent relocations, migratory instability, lack of social support, and psychological distress related to past traumas further exacerbate these women's vulnerability, negatively impacting maternal and child outcomes. As improvement strategies, the literature emphasizes the importance of integrated, culturally responsive care models, including qualified interpreters, psychosocial support, health education, community leadership engagement, and specialized multidisciplinary teams. Implementing public policies that recognize the complexity of refugee women’s trajectories and ensure care continuity is critical for equitable, humane, and safe perinatal care. In conclusion, while significant progress has been made in developing policies and guidelines for perinatal care for refugee women, substantial gaps remain in implementing effective and welcoming practices. The literature underscores the need for stronger, sustainable public policies to expand access and ensure equity in perinatal healthcare for refugee populations, particularly in high-vulnerability settings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,014 | 0,014 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».