Dialysis nurse demand in Europe: an estimated prediction based on modelling
Bibliographic record
Abstract
Background: To estimate the projections of supply and demand for dialysis nurses (DNs) over 5 years in four European countries (France, Italy, Spain and the UK). Methods: This study modelled the nursing labour workforce across each jurisdiction by estimating the current nursing labour force, number of nursing graduates and the attrition rate. Results: France currently has the greatest demand for DNs (51 325 patients on dialysis), followed by Italy, the UK and Spain with 40 661, 30 301 and 28 007 patients on dialysis, respectively. The number of in-centre haemodialysis (HD) patients is expected to increase in the four countries, while the number of patients on home HD (HHD) or on peritoneal dialysis (PD) is expected to increase in the UK. Currently Italy has the greatest proportion of DNs (2.6%), followed by France (2.1%), Spain (1.7%) and the UK (1.5%). Estimation of the dialysis nursing staff growth rate over 5 years showed that the UK has the greatest estimated growth rate (6%), followed by Italy (2%), France (2%) and Spain (1%). Conclusions: Dialysis demand will increase in the coming years, which may exacerbate the DN shortage. Additionally, competencies and training requirements of DNs should be precisely defined. Finally, implementing and facilitating PD and HHD strategies would be helpful for patients, healthcare professionals and healthcare systems and can even help ease the DN shortage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".