Descriptive, cross-country analysis of the nurse practitioner workforce in six countries: size, growth, physician substitution potential
Bibliographic record
Abstract
OBJECTIVES: Many countries are facing provider shortages and imbalances in primary care or are projecting shortfalls for the future, triggered by the rise in chronic diseases and multimorbidity. In order to assess the potential of nurse practitioners (NPs) in expanding access, we analysed the size, annual growth (2005-2015) and the extent of advanced practice of NPs in 6 Organisation for Economic Cooperation and Development (OECD) countries. DESIGN: Cross-country data analysis of national nursing registries, regulatory bodies, statistical offices data as well as OECD health workforce and population data, plus literature scoping review. SETTING/PARTICIPANTS: NP and physician workforces in 6 OECD countries (Australia, Canada, Ireland, the Netherlands, New Zealand and USA). PRIMARY AND SECONDARY OUTCOME MEASURES: The main outcomes were the absolute and relative number of NPs per 100 000 population compared with the nursing and physician workforces, the compound annual growth rates, annual and median percentage changes from 2005 to 2015 and a synthesis of the literature on the extent of advanced clinical practice measured by physician substitution effect. RESULTS: The USA showed the highest absolute number of NPs and rate per population (40.5 per 100 000 population), followed by the Netherlands (12.6), Canada (9.8), Australia (4.4), and Ireland and New Zealand (3.1, respectively). Annual growth rates were high in all countries, ranging from annual compound rates of 6.1% in the USA to 27.8% in the Netherlands. Growth rates were between three and nine times higher compared with physicians. Finally, the empirical studies emanating from the literature scoping review suggested that NPs are able to provide 67-93% of all primary care services, yet, based on limited evidence. CONCLUSIONS: NPs are a rapidly growing workforce with high levels of advanced practice potential in primary care. Workforce monitoring based on accurate data is critical to inform educational capacity and workforce planning.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".