Barriers and effective interventions associated with diabetes management among the East Asian immigrant population: A scoping review
Notice bibliographique
Résumé
BACKGROUND: The prevalence of diabetes has rapidly increased for East Asian immigrant populations, exceeding rates in East Asian populations in their home countries and the general population of host countries. The increased risk highlights the complex interplay between genetic predisposition and socio-cultural environmental factors associated with migration. Managing diabetes and navigating unfamiliar healthcare systems are challenging for immigrant populations underscoring the need for research on barriers and effective targeted strategies. OBJECTIVES: Identify barriers to best diabetes management practices and effective interventions among East Asian immigrant populations. METHODS: Studies were identified through PUBMED, OVID MEDLINE, CINAHL COMPLETE and SCOPUS databases utilising Arksey and O'Malley's framework. Peer-reviewed, English reports between January 2010 and August 2024 relating to challenges and barriers of best management practices among adult East Asian immigrants and interventions that facilitated management were identified. Studies of people with type 1 or gestational diabetes and those <18 years old were excluded. RESULTS: Of 576 articles screened, 18 studies meeting the criteria were included in this review. Twelve studies included Chinese immigrants, 13 studies were from the United States, including six among American-Korean immigrants, three were from Australia and two from Canada. Barriers to best diabetes management practices identified from observational studies were themed as relating to (1) 'Cultural Views' (diabetes, diet, medication, traditional remedies, health professional hierarchy and family roles); (2) 'Immigration Challenges' (communication, communication, transport, financial, time constraints, emotional distress and dissatisfaction with Western healthcare systems). Randomised controlled trials (n = 2) and single-group trials (n = 6) reported on effective interventions that improved self-management and/or cardiometabolic risk factors, focusing on self-management (n = 3), nutritional (n = 4) and social media (n = 1) educational programmes. CONCLUSION: Barriers to best diabetes management practices included clashes with cultural views, immigration-related challenges and dissatisfaction with Western healthcare systems. Effective interventions were mostly associated with culturally-tailored, didactic and bilingual diabetes education programmes.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».