Bibliographic record
Abstract
Many countries are seeking to improve health care delivery by reviewing the roles of health professionals, including nurses. Developing new and more advanced roles for nurses could improve access to care in the face of a limited or diminishing supply of doctors. It might also contain costs by delegating tasks away from more expensive doctors. This paper reviews the development of advanced practice nurses in 12 countries (Australia, Belgium, Canada, Cyprus, Czech Republic, Finland, France, Ireland, Japan, Poland, United Kingdom and United States), with a particular focus on their roles in primary care. It also reviews the evaluations of impacts on patient care and cost. The development of new nursing roles varies greatly. The United States and Canada established “nurse practitioners” in the mid-1960s. The United Kingdom and Finland also have a long experience in using different forms of collaboration between doctors and nurses. Although development in Australia and Ireland is more recent, these two countries have been very active in establishing higher education programmes and posts for advanced practice nurses in recent years. In other countries, the formal recognition of advanced practice nurses is still in its infancy, although unofficial advanced practices may already exist in reality. Evaluations show that using advanced practice nurses can improve access to services and reduce waiting times. Advanced practice nurses are able to deliver the same quality of care as doctors for a range of patients, including those with minor illnesses and those requiring routine follow-up. Most evaluations find a high patient satisfaction rate, mainly because nurses tend to spend more time with patients, and provide information and counselling. Some evaluations have tried to estimate the impact of advanced practice nursing on cost. When new roles involve substitution of tasks, the impact is either cost reducing or cost neutral. The savings on nurses’ salaries – as opposed to doctors – can be offset by longer consultation times, higher patient referrals, and sometimes the ordering of more tests. When new roles involve supplementary tasks, some studies report that the impact is cost increasing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".