Developing nephrology services in low income countries: a case of Tanzania
Bibliographic record
Abstract
BACKGROUND: The burden of kidney diseases is reported to be higher in lower- and middle-income countries as compared to developed countries, and countries in sub-Saharan Africa are reported to be most affected. Health systems in most sub-Sahara African countries have limited capacity in the form of trained and skilled health care providers, diagnostic support, equipment and policies to provide nephrology services. Several initiatives have been implemented to support establishment of these services. METHODS: This is a situation analysis to examine the nephrology services in Tanzania. It was conducted by interviewing key personnel in institutions providing nephrology services aiming at describing available services and international collaborators supporting nephrology services. RESULTS: Tanzania is a low-income country in Sub-Saharan Africa with a population of more than 55 million that has seen remarkable improvement in the provision of nephrology services and these include increase in the number of nephrologists to 14 in 2018 from one in 2006, increase in number of dialysis units from one unit (0.03 unit per million) before 2007 to 28 units (0.5 units per million) in 2018 and improved diagnostic services with introduction of nephropathology services. Government of Tanzania has been providing kidney transplantation services by funding referral of donor and recipients abroad and has now introduced local transplantation services in two hospitals. There have been strong international collaborators who have supported nephrology services and establishment of nephrology training in Tanzania. CONCLUSION: Tanzania has seen remarkable achievement in provision of nephrology services and provides an interesting model to be used in supporting nephrology services in low income countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".