A global assessment of kidney care workforce
Bibliographic record
Abstract
BACKGROUND: An adequate workforce is needed to guarantee optimal kidney care. We used the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) to provide an assessment of the global kidney care workforce. METHODS: We conducted a multinational cross-sectional survey to evaluate the global capacity of kidney care and assessed data on the number of adult and paediatric nephrologists, the number of trainees in nephrology and shortages of various cadres of the workforce for kidney care. Data are presented according to the ISN region and World Bank income categories. RESULTS: Overall, stakeholders from 167 countries responded to the survey. The median global prevalence of nephrologists was 11.75 per million population (pmp) (interquartile range [IQR] 1.78-24.76). Four regions had median nephrologist prevalences below the global median: Africa (1.12 pmp), South Asia (1.81 pmp), Oceania and Southeast Asia (3.18 pmp) and newly independent states and Russia (9.78 pmp). The overall prevalence of paediatric nephrologists was 0.69 pmp (IQR 0.03-1.78), while overall nephrology trainee prevalence was 1.15 pmp (IQR 0.18-3.81), with significant variations across both regions and World Bank income groups. More than half of the countries reported shortages of transplant surgeons (65%), nephrologists (64%), vascular access coordinators (59%), dialysis nurses (58%) and interventional radiologists (54%), with severe shortages reported in low- and lower-middle-income countries. CONCLUSIONS: There are significant limitations in the available kidney care workforce in large parts of the world. To ensure the delivery of optimal kidney care worldwide, it is essential to develop national and international strategies and training capacity to address workforce shortages.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".