Task-shifting from physicians to nurses in Europe and other major OECD countries
Bibliographic record
Abstract
Background Task-shifting has been implemented in the United States, Canada, Australia and New Zealand and increasingly in Europe. A cross-country comparison of task-shifting has been lacking across Europe. We assessed task-shifting practices in Europe and other OECD countries, and secondly, performed correlation analyses with OECD data. Methods A survey was developed, pilot tested and sent to 109 country informants in 39 countries covering Europe, the United States, Canada, Australia and New Zealand (response rate 85.3%). Country informants were chosen based on a pre-defined set of criteria. Countries levels of implementation was correlated with OECD secondary data: physician and nurse ratios, education, and primary mode of financing (fee-for-service vs other). Results Eleven countries have implemented extensive task-shifting (Australia, Canada, New Zealand, the Netherlands, US, UK (England, Wales, N. Ireland, Scotland), Finland, Ireland), measured by authority to diagnose, refer, treat and prescribe. However, countries' levels of regulation and financing varied, as did training requirements. The majority of countries showed emerging, yet limited task-shifting where nurses took up some advanced roles within confined boundaries. Five countries did not implement task-shifting. Conclusions Countries most advanced showed variations of the regulatory contexts, which may impact on nurses' practice patterns. Countries with decentralized regulation resulted in uneven levels of implementation, posing barriers to an efficient use of this workforce. Countries in early development stages focused primarily on adapting training capacity. From an international and especially, EU perspective, harmonizing competencies and training – in those countries showing similar levels of advanced practice – will be an important step to ensure the quality of care, avoid potential skill-loss and facilitate the recognition of education in increasingly connected labor markets. Key messages Task-shifting from physicians to nurses is an increasing workforce trend in Europe, however, extent of task-shifting and levels of implementation vary An enabling policy context involves up-to-date regulation, quality education pipeline and a supportive financing structure
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".