Healthcare Service Utilisation Across Continuum of Care for Type 2 Diabetes Among Culturally and Linguistically Diverse Populations: A Systematic Review
Bibliographic record
Abstract
Introduction: Healthcare service utilisation for type 2 diabetes (T2D) is suboptimal among people from culturally and linguistically diverse (CALD) backgrounds. Synthesised evidence on drivers influencing their healthcare access and utilisation across the continuum of care (CoC) is scarce. This review synthesised drivers of accessing and utilising healthcare services across the CoC for T2D from the perspectives of people from CALD backgrounds and their healthcare service providers (HSPs). Methods: Five databases (Scopus, PubMed, Web of Science, CINAHL, and PsycINFO) were searched from inception to November 2023. This review was prospectively registered with PROSPERO (ID: CRD42023491560). The McGill Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of articles included in this systematic review. Studies were included if they were primary research involving people from CALD backgrounds and/or their HSPs, and reported data related to access to and utilisation of services across the CoC. The CoC framework was used to conduct a guided thematic analysis for qualitative findings and narrative synthesis was used to summarise quantitative findings. Results: Thirty-five studies were included: 31 qualitative, 3 quantitative, and 1 mixed-method. Psychosocial (e.g., fear of treatment) and sociocultural barriers (e.g., stigma) were reportedly encountered at diagnosis and initiation of treatment. Language and cultural barriers were expressed by most people with T2D and their HSPs, especially at the monitoring and adherence stages of the CoC. Trusted relationships with HSPs and the cultural competency of the HSPs were facilitators of continued monitoring and adherence and long-term care and management. No barriers or facilitators were identified for decision-making to enter the healthcare systems, screening, and first point of contact aspects of the CoC from the perspectives of either patients or HSPs. Conclusions: Although included articles were heavily skewed towards qualitative studies, the synthesised evidence on factors influencing access to and utilisation of healthcare services for T2D can inform policymaking by highlighting pathways to improved use of healthcare services among these groups.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".