Realist review to understand the efficacy of culturally appropriate diabetes education programmes
Bibliographic record
Abstract
AIMS: Minority populations often face linguistic, cultural and financial barriers to diabetes education and care. The aim was to understand why culturally appropriate diabetes education interventions work, when they work best and for whom they are most effective. METHODS: This review used a critical realist approach to examine culturally appropriate diabetes interventions. Beginning with the behavioural model and access to medical care, it reanalysed 11 randomized controlled trials from a Cochrane systematic review and related programme and training documents on culturally appropriate diabetes interventions. The analysis examined context and mechanism to understand their relationship to participant retention and statistically improved outcomes. RESULTS: Minority patients with language barriers and limited access to diabetes programmes responded to interventions using health workers from the same ethnic group and interventions promoting culturally acceptable and financially affordable food choices using local ingredients. Programme incentives improved retention in the programmes and this was associated with improved HbA(1c) levels at least in the short term. Adopting a positive learning environment, a flexible and less intensive approach, one-to-one teaching in informal settings compared with a group approach in clinics led to improved retention rates. CONCLUSIONS: Minority and uninsured migrants with unmet health needs showed the highest participation and HbA(1c) responses from culturally appropriate 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 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 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".