Task shifting from physicians to nurses in primary care in 39 countries: a cross-country comparative study
Bibliographic record
Abstract
BACKGROUND: Primary care is in short supply in many countries. Task shifting from physicians to nurses is one strategy to improve access, but international research is scarce. We analysed the extent of task shifting in primary care and policy reforms in 39 countries. METHODS: Cross-country comparative research, based on an international expert survey, plus literature scoping review. A total of 93 country experts participated, covering Europe, USA, Canada, Australia and New Zealand (response rate: 85.3%). Experts were selected according to pre-defined criteria. Survey responses were triangulated with the literature and analysed using policy, thematic and descriptive methods to assess developments in country-specific contexts. RESULTS: Task shifting, where nurses take up advanced roles from physicians, was implemented in two-thirds of countries (N = 27, 69%), yet its extent varied. Three clusters emerged: 11 countries with extensive (Australia, Canada, England, Northern Ireland, Scotland, Wales, Finland, Ireland, Netherlands, New Zealand and USA), 16 countries with limited and 12 countries with no task shifting. The high number of policy, regulatory and educational reforms, such as on nurse prescribing, demonstrate an evolving trend internationally toward expanding nurses' scope-of-practice in primary care. CONCLUSIONS: Many countries have implemented task-shifting reforms to maximise workforce capacity. Reforms have focused on removing regulatory and to a lower extent, financial barriers, yet were often lengthy and controversial. Countries early on in the process are primarily reforming their education. From an international and particularly European Union perspective, developing standardised definitions, minimum educational and practice requirements would facilitate recognition procedures in increasingly connected labour markets.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".