Barriers and strategies in care delivery for type 1 diabetes in Sub-Saharan Africa: a scoping review
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes is one of the most common chronic diseases in children. More than 1.2 million young people under the age of 20 are affected, the majority of whom live in low-income countries. The care delivery of type 1 diabetes in sub-Saharan Africa is an important part of protecting children's health, preventing complications, and supporting families in vulnerable populations. This scoping review aims to describe and categorize the most important barriers to type 1 diabetes care delivery in Sub-Saharan Africa and provides an overview of the types of strategies that are used to overcome these barriers. METHODS: Scoping review methodology was used. Five online databases and a general web search for grey literature were searched. Eligibility criteria were applied by two reviewers, and data were extracted and analysed by one reviewer. All studies published or unpublished from 1990 until the end of 2020 were considered. The content was analyzed using a qualitative synthesis approach. RESULTS: Research, including publications from 1990 to 2019, identified 504 studies, narrowed down to 38 after review. All studies were based in sixteen Sub-Saharan African countries, limiting the generalizability of our results. Barriers to the care delivery of type 1 diabetes in Sub-Saharan Africa are related to contextual factors at several levels: health system, policy, structure and performance, patient factors, and socio-cultural factors. The core elements of successful strategies for care delivery of type 1 diabetes are education, staff training, and social support. CONCLUSIONS: Available evidence indicates that structured care delivery can improve the health of people living with type 1 diabetes in Sub-Saharan Africa. Obstacles in the case and population care delivery of type 1 diabetes and the health of people living with type 1 diabetes need to be analyzed before strategies adapted to the context and specific target groups can be developed.
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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 | 0.028 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".