Building optimal and sustainable kidney care in low resource settings: The role of healthcare systems
Bibliographic record
Abstract
Healthcare systems in low-income and lower-middle income countries (LLMICs) face significant challenges in the provision of health services, for example, kidney care to the population. Although this is linked to several high-level factors such as poor infrastructure, socio-demographic and political factors, healthcare funding has often been cited as the major reason for the wide gap in availability, accessibility and quality of care between LLMICs and rich countries. With the steady rising incidence and prevalence of kidney diseases globally, as well as cost of care, LLMICs are likely to suffer more consequences of these increases than rich countries and may be unable to meet targets of universal health coverage (UHC) for kidney diseases. As health systems in LLMICs continue to adapt in finding ways to provide access to affordable kidney care, various empirical and evidence-based strategies can be applied to assist them. This review uses a framework for healthcare strengthening developed by the World Health Organization (WHO) to assess various challenges that health systems in LLMICs confront in providing optimal kidney care to their population. We also suggest ways to overcome these barriers and strengthen health systems to improve kidney care in LLMICs.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".