A Citation Analysis of the Top 50 Most Cited Nurse Practitioner Publications
Bibliographic record
Abstract
The role of nurse practitioners (NPs) has become integral to healthcare systems worldwide. Originating in the United States over 50 years ago, it has since been adopted by countries such as Canada, the United Kingdom, and Australia. To honor the research and contributions that have shaped the NP discipline, it is valuable to review and recognize key literature that has significantly impacted its development. Bibliometrics, a research methodology, offers an objective lens for evaluating the influence of scholarly articles on the evolution of a discipline. Citation analysis (CA), a key method in bibliometrics, examines how frequently a publication is cited by others, often serving as a measure of its impact, influence, and contribution to its field. This study aims to identify the top 50 most cited publications related to NPs in the Web of Science (WoS) database to review and describe the influential works that have contributed to the profession's growth. Comparisons are drawn with a parallel review in Scopus and recent NP-related bibliometric studies. In 2021, a structured search was conducted using the WoS Core Collection with key terms such as "Nurse Practitioner" and "Advanced Practice Nurse". Inclusion and exclusion criteria were applied, and publications were ranked by citation count from highest to lowest. The analysis covered citation counts, topics, publication dates/types, country of origin, author details (institution and discipline), and journal characteristics (e.g., impact factor, IF). The top 50 most cited articles and their characteristics are presented. Citation counts ranged from 78 to 656, with publication dates spanning six decades across 30 journals, 38 institutions, and 194 authors. The leading authors were Mary O'Neil Mundinger, Denise Bryant-Lukosius, and Alba DiCenso. Topics included the role's impact and development, registration/licensing, and scope of practice. Most articles (n = 35) were published in journals with an IF greater than 2. This review offers a systematic approach to identifying seminal works that have influenced the NP profession globally. While CA has its limitations, it provides a valuable method for literature review. This study contributes valuable insights into the history and development of NP research and offers guidance for future research efforts.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.110 | 0.126 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".