Workforce capacity for the care of patients with kidney failure across world countries and regions
Bibliographic record
Abstract
INTRODUCTION: An effective workforce is essential for optimal care of all forms of chronic diseases. The objective of this study was to assess workforce capacity for kidney failure (KF) care across world countries and regions. METHODS: Data were collected from published online sources and a survey was administered online to key stakeholders. All country-level data were analysed by International Society of Nephrology region and World Bank income classification. RESULTS: The general healthcare workforce varies by income level: high-income countries have more healthcare workers per 10 000 population (physicians: 30.3; nursing personnel: 79.2; pharmacists: 7.2; surgeons: 3.5) than low-income countries (physicians: 0.9; nursing personnel: 5.0; pharmacists: 0.1; surgeons: 0.03). A total of 160 countries responded to survey questions pertaining to the workforce for the management of patients with KF. The physicians primarily responsible for providing care to patients with KF are nephrologists in 92% of countries. Global nephrologist density is 10.0 per million population (pmp) and nephrology trainee density is 1.4 pmp. High-income countries reported the highest densities of nephrologists and nephrology trainees (23.2 pmp and 3.8 pmp, respectively), whereas low-income countries reported the lowest densities (0.2 pmp and 0.1 pmp, respectively). Low-income countries were most likely to report shortages of all types of healthcare providers, including nephrologists, surgeons, radiologists and nurses. CONCLUSIONS: Results from this global survey demonstrate critical shortages in workforce capacity to care for patients with KF across world countries and regions. National and international policies will be required to build a workforce capacity that can effectively address the growing burden of KF and deliver optimal care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".