Experiences of nurses caring for involuntary migrant maternal women: a qualitative systematic review
Notice bibliographique
Résumé
OBJECTIVE: The objective of this review was to identify, critically appraise, and synthesize qualitative evidence on the experiences of nurses providing care within various health care delivery environments to involuntary migrant women who are experiencing pregnancy, birth, or post-birth. INTRODUCTION: Nurses are central to providing care to populations experiencing inequities. These populations include forcibly displaced pregnant and/or mothering women who have migrated involuntarily. Most of these women are ethnically diverse and often experience poverty and low literacy. This review is focused on the experiences of nurses providing care to these women. INCLUSION CRITERIA: This review considered qualitative, peer-reviewed studies published in academic journals. Studies and study abstracts that examined nurses' experiences of providing care to involuntary migrant maternal women were included. Women could be pregnant and/or mothering. All settings in which nurses practice were considered. METHODS: Information sources that were systematically searched for this review included CINAHL (EBSCO), PsycINFO (EBSCO), MEDLINE (EBSCO), PubMed (NLM), Web of Science, and Google Scholar. A gray literature search in Google was also developed. Studies published in English from 2000 onward were considered. Final searches were conducted in January 2021 using language within database thesauruses, such as CINAHL headings and MeSH terms, as well as keywords related to qualitative inquires on experiences of nurses caring for involuntary migrant maternal women. An intersectionality lens was applied within all review methods. Study selection was conducted by two reviewers who screened titles and abstracts that aligned with the inclusion criteria. The review followed the JBI approach for critical appraisal, data extraction, and data synthesis. RESULTS: Twenty-three qualitative studies were included in this review. Qualitative methodologies within these studies included case study, ethnography, interpretive descriptive, and grounded theory. Nine studies considered the sex of participating nurses, and three studies considered participant history of migration. A total of 115 findings were pooled into four categories and aggregated into the following two synthesized findings: i) Nurses integrate cultural and linguistic diversity within practice; and ii) Nurses assess for inequities resulting from forced migration on maternal women. Study quality was rated as moderate on ConQual scoring, with dependability rated as moderate and credibility rated as high. CONCLUSIONS: Key implications are made within nursing education programming, nursing practice, and policy analysis. In the realm of nursing education, integration of migrant status as a health determinant will enhance nurses' skills in assessing migrant status and understanding how varying statuses contribute to barriers among involuntary migrant women accessing health services. Providing ongoing education to nurses centered on trauma and violence-informed practice is recommended. With regard to nursing practice, review findings revealed the need for creative solutions to overcome language barriers. Innovative approaches for nurses working across language barriers in acute and community health contexts when interpreter services are not available need further exploration and protocol integration. Examination of clinical care pathways is needed for inclusion of involuntary migrant women, and exploring assessment strategies targeting how migrant status contributes to limited health service accessibility. For policy, organizations need to build policies that promote examination of migrant status and its health impacts among involuntary migrant maternal women exposed to migration-related trauma and violence to support nurses in their care provision. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42019137922.
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 | 0,029 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».