Promoting lifestyle changes in patients with prediabetes from African-Caribbean backgrounds in the United Kingdom
Bibliographic record
Abstract
OBJECTIVES: Diabetes is a non-communicable disease where the patient's glucose level in the blood is too high. Diabetes is prevalent among ethnic minority groups in the United Kingdom (UK). Type 2 diabetes is a major cause of premature mortality in England. Unfortunately, the lifestyle of these minority groups has become a barrier to diabetes healthcare treatment. The timely intervention of programmes targeting risk factors associated with diabetes may reduce the prevalence of diabetes among these ethnic minority groups. This review critically explores and identifies barriers that hinder specific African-Caribbean groups from accessing diabetes healthcare and how nurses can promote lifestyle changes in patients with prediabetes from African-Caribbean backgrounds. DESIGN: An extended literature review (ELR). The process consisted of a search of key databases and other nursing and public health journal articles with the keywords defined in this extended review (prediabetes, diabetes, lifestyle of Afro-Caribbean). Thematic analysis is then applied from a socio-cultural theoretical lens to interpret the selected articles for the review. RESULTS: Three main barriers were identified: (a) the strong adherence to traditional diets, (b) a wrong perception about diet management and (c) 'Western medication' as a key barrier that hinders effective diabetes management in ethnic minorities, including the African-Caribbean in the UK. CONCLUSION: To address these barriers, it is important for policymakers to prioritise well-tailored interventions for African-Caribbean groups as well as support healthcare providers with the requisite capacity to provide care.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".