Doctoral education, advanced practice and research: An analysis by nurse leaders from countries within the six WHO regions
Bibliographic record
Abstract
Doctoral education, advanced practice and research are key elements that have shaped the advancement of nursing. Their impact is augmented when they are integrated and synergistic. To date, no publications have examined these elements holistically or through an international lens. Like a three-legged stool they are inter-reliant and interdependent. Research is integral to doctoral education and influential in informing best practice. This significance and originality of this discussion paper stem from an analysis of these three topics, their history, current status and associated challenges. It is undertaken by renowned leaders in 11 countries within the six World Health Organisation (WHO) regions: South Africa, Egypt, Finland, United Kingdom, Brazil, Canada, United States, India, Thailand, Australia, and the Republic of Korea. The first two authors used a purposive approach to identify nine recognized nurse leaders in each of the six WHO regions. These individuals have presented and published papers on one or more of the three topics. They have led, or currently lead, large strategic organisations in their countries or elsewhere. All these accomplished scholars agreed to collect relevant data and contribute to the analysis as co-authors. Doctoral education has played a pivotal role in advancing nurse scholarship. Many Doctor of Philosophy (PhD) prepared nurses become faculty who go on to educate and guide future nurse researchers. They generate the evidence base for nursing practice, which contributes to improved health outcomes. In this paper, the development of nursing doctoral programmes is examined. Furthermore, PhDs and professional doctorates, including the Doctor of Nursing Practice, are discussed, and trends, challenges and recommendations are presented. The increasing number of advanced practice nurses worldwide contributes to better health outcomes. Nonetheless, this paper shows that the role remains absent or underdeveloped in many countries. Moreover, role ambiguity and role confusion are commonplace and heterogeneity in definitions and titles, and regulatory and legislatorial inconsistencies limit the role's acceptance and adoption. Globally, nursing research studies continue to increase in number and quality, and nurse researchers are becoming partners and leaders in interdisciplinary investigations. Nonetheless, this paper highlights poor investment in nursing research and a lack of reliable data on the number and amount of funding obtained by nurse researchers. The recommendations offered in this paper aim to address the challenges identified. They have significant implications for policy makers, government legislators and nurse leaders.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".