Barriers and effective interventions associated with diabetes management among the East Asian immigrant population: A scoping review
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".