Challenges and opportunities in screening and management of hypertension and type 2 diabetes in informal settlements in Nairobi, Kenya: A qualitative study of multi-sectoral stakeholders
Bibliographic record
Abstract
The purpose of the study is to explore barriers and facilitators to noncommunicable disease (NCD) screening and management in Nairobi's informal settlements to inform future NCD programme development. Semi-structured interviews were conducted among 38 multi-sectoral stakeholders, comprising of community health workers (CHWs), nurses, clinical officers, physicians, community-based organisation (CBO) staff, individuals living with hypertension or type 2 diabetes (T2D), and individuals who are eligible for screening but never screened. Workforce shortages and medication unavailability were raised as primary barriers by all stakeholders in informal settlements. Misconceptions and stigma around NCDs contributed to social isolation for people living with NCDs and hindered them from getting the health care and social support they need. Fragmented NCD registries contributed to resource misallocation. Long waiting times, poverty and competing life demands further hindered care access. Several opportunities to overcome challenges in NCD care were also identified. Many advocated for supporting and leveraging CHWs to enhance community-based NCD programs, recognising their potential to address gaps in healthcare access. Strengthening patient support groups and expanding community outreach were proposed as strategies to raise public awareness, provide social support and improve care accessibility. A top-down, multi-level intervention approach is needed to improve health equity for these communities.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".