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Article

Citation Network Analysis of Nurse Staffing Research from the Past Two Decades: 2000–2022

by
Noriko Morioka
1,*,
Masanao Ochi
2,
Suguru Okubo
3,
Mutsuko Moriwaki
4,
Kenshi Hayashida
5,
Ichiro Sakata
2 and
Masayo Kashiwagi
1
1
Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan
2
Graduate School of Engineering, University of Tokyo, Bunkyo-Ku, Tokyo 113-8656, Japan
3
Institute of Ars Vivendi, Ritsumeikan University, Kyoto 603-8577, Japan
4
Quality Management Center Medical Hospital, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan
5
Department of Medical Informatics and Management, University Hospital, University of Occupational and Environmental Health, Fukuoka 807-8555, Japan
*
Author to whom correspondence should be addressed.
Healthcare 2023, 11(23), 3050; https://doi.org/10.3390/healthcare11233050
Submission received: 3 October 2023 / Revised: 20 November 2023 / Accepted: 20 November 2023 / Published: 27 November 2023
(This article belongs to the Section Healthcare Quality and Patient Safety)
Browse Figures
Versions Notes

Abstract

:
Studies have indicated that higher numbers of nurses regarding staffing ensure patient safety and a better practice environment. Using citation analysis, this study visualizes the landscape of nurse staffing research over the last two decades to show the overall publication trends, major contributors, and main research topics. We extracted bibliometric information from PubMed from January 2000 to September 2022. After clustering the network, we analyzed each cluster’s characteristics by keyword. A total of 2167 papers were considered for analysis, and 14 clusters were created. The analysis showed that the number of papers published per year has been increasing. Researchers from the US, the UK, Canada, Australia, and Belgium have led this field. As the main clusters in nurse staffing research during the past two decades, the following five research settings were identified: nurse outcome and patient outcome research in acute care hospitals, nurse staffing mandate evaluation research, nursing home research, and school nurse research. The first three clusters accounted for more than 80% of the total number of published papers, and this ratio has not changed in the past 20 years. To further develop nurse staffing research globally, evidence from other geographic areas, such as African and Asian countries, and from long-term care or community settings is necessary.

1. Background

Worldwide, approximately 20 million nursing staff, accounting for more than 70% of healthcare professionals, provide patient care on the front lines 24 h a day, 365 days a year [1]. The efficient and effective allocation of nursing staff is a major component of establishing a quality healthcare system. Since early 2000, in the context of developing indicators for quality assurance, the nurse-to-patient ratio and skill mix have been considered as some of the nursing sensitivity indicators [2]. Several studies on appropriate nurse staffing have been conducted since the landmark studies of the early 2000s [3,4]. Analyses of these findings from systematic and umbrella reviews have shown robust evidence for an association between higher nursing staffing and a shorter length of stay, increased patient satisfaction, improved quality of nursing care, fewer readmissions, and reduced in-hospital mortality [5,6,7,8,9,10,11].
Concurrently, to ensure safe nurse staffing, several countries and states have introduced legislation or financial incentives related to nurse staffing ratios in hospitals and other facilities. California was the first US state to enact a minimum nurse-to-patient staffing ratio law in 2004 [12,13]. The state currently requires a minimum ratio of one nurse to five patients in medical–surgical units and one nurse to two patients in critical care units. Massachusetts, USA, requires a 1:1 nurse-to-patient ratio in ICUs. The New York State enacted the “Safe Staffing for Quality Care Act” for acute care facilities and nursing homes in 2021. A roughly 2:1 patient-to-nurse ratio in intensive care units or nursing homes requires facilities to meet a minimum daily average of three and a half hours of nursing care per resident [14]. In Australia, Victoria [15] and Queensland [16] have established regulations and laws regarding the minimum required nurse-to-patient ratio in hospitals, with one nurse to four patients for the day shift and one nurse to seven patients for the night shift. Japan [17] and South Korea [18] have also introduced nurse staffing levels into their financial schemes as patient-to-nurse ratios differentiate the reimbursement of nursing fees. In Japan, there are four categories of nurse staffing in hospitals as follows: the 7:1 patient-to-nurse ratio, 10:1, 13:1, and 15:1. In South Korea, nursing fees are determined by the hospital type and combination of patient-to-registered nurse ratios and patient-to-nursing assistant or ward staff ratios [17]. The UK does not have legal nurse staffing mandates [19]; however, the National Institute for Health and Care Excellence has introduced guidelines for safe nurse staffing levels [20].
To improve nurse staffing policies, it is important to conduct policy evaluations of these regulations in various countries and/or states. In countries without legislation, it is necessary to undertake research on safe nursing placement that captures the characteristics of the country’s healthcare system. However, a geographic bias has been indicated in previous research, which has mostly presented evidence from high-income countries [21,22]. In addition, nurses are active in both hospitals and other areas [1], and there is a lack of evidence and regulations on safe nurse staffing in long-term care settings [23,24] and community settings [25,26]. Appropriate nursing assignments are important not only in acute care settings in acute care hospitals but also in long-term care settings and community settings.
Recently, with the advancement of language processing technology, “bibliometric (or citation network) analysis”, which analyzes bibliographic information, has become increasingly useful in showing the landscape and identifying the major topics of a health service research [27,28]. In nursing research, the themes of “magnet hospital” [29], “nursing student education” [30], or “nurse resilience” [31] have been used to visualize research trends and issues. Regarding nurse staffing research, studies have synthesized the evidence using systematic and umbrella reviews that focus on specific settings and outcomes, for example, intensive care units [9] and mortality in acute care hospitals [6,8,32], and study designs, such as longitudinal studies [10]. As the research on nurse staffing has grown enormously, it has become difficult for traditional manual literature review methods to provide a bird’s-eye view of the entire research area and its trends and hot spots. Bibliometric (or citation network) analysis methods can contribute to uncovering research gaps within nurse staffing research that have not yet been identified and research that remains untapped. Identifying this research gap can ultimately contribute to developing healthcare systems that benefit patients and nurses worldwide.
Consequently, this study aimed to visualize the landscape of nurse staffing research over the past two decades. It used citation analysis to show the overall trends in the number of publications and major contributors and reveal the main research topics and the trends in these topics based on similarities in citation relationships.

2. Methods Section

2.1. Design and Data Collection

A citation network analysis was conducted. We extracted bibliometric information from PubMed. The search strategy was “nurse staffing” [Title/Abstract] OR “nurse workload” [Title/Abstract] OR “nurse workforce” [Title/Abstract] OR “nurse to patient” [Title/Abstract] OR “patient to nurse” [Title/Abstract] OR “nurse to bed” [Title/Abstract]. We set the publication limit from 1 January 2000 to 30 September 2022 (Appendix A Table A1).
In the search process, unlike traditional literature review methods, all the literature that met the above search strategy was included in the citation analysis. No limitation was imposed on the study setting. Nursing positions or titles were included, ranging from general nursing staff to advanced practice nurses. We also included a broad range of the literature with titles or abstracts in English, even if the main text was in a different language.

2.2. Analysis

2.2.1. Citation Network Analysis

We used the “academic-landscape system” at the University of Tokyo, Japan (https://academic-landscape.com/page/about?next=/ accessed on 1 October 2022) (Copyright © 2010–2013 Innovation Policy Research Center, The University of Tokyo.) for the analysis. This system was developed to visualize citation network analyses through the following procedures: (1) retrieving data, (2) determining the maximum connected component and network clustering, and (3) visualization [33]. We performed citation analysis by combining direct citation relationships (when a paper directly cites other papers and analyzing these citation relationships helps us understand the direct connections between papers), co-citation relationships (when multiple papers cite the same other paper and these papers are in a co-citation relationship), bibliographic coupling (when using the same references in their citations, the similarity between papers can be assessed through bibliographic coupling), weighting functions (when representing relationships between papers, assigning weights to each relationship is often used, which allows the importance of different relationships to be considered), keyword similarity (cosine similarity) (by treating keywords in papers as vector representations and calculating the cosine similarity between keyword vectors, the similarity in the content between papers can be evaluated), and modularity maximization (a method that uses the network theory to identify modules or communities within a network) [34,35] In the clustering process, papers that have no direct citation relationship with other papers are automatically excluded from the analysis.
The size of the clusters indicates the number of papers included, and the spatial distance between clusters indicates the similarity of the content. Clusters that are far apart in spatial distance indicate independent research topics with little citation relationship. More details of the analysis are described in Kajikawa et al. [33,36]. The schematic analysis procedure is illustrated in Appendix A, Figure A1.
After clustering the network, we analyzed each cluster’s characteristics by the keywords with higher scores of term frequency-inversed cluster frequency (TF-ICF), titles, and abstracts in papers that were frequently cited by other papers in the cluster in which they were published. TF-ICF is a vector representation of natural language and is an effective method for extracting important words that characterize a cluster. The following equations calculated TF-ICF:
TF-ICF = TF × ICF
where
TF = the term (keyword) counts in the cluster/total word counts in the cluster,
ICF = log[1 − (the total number of clusters/number of cluster including the term)]
The TF-ICF scores were higher for words that occurred more frequently only in the cluster. A higher TF-ICF score indicates more importance regarding the word that characterizes the cluster.

2.2.2. Analysis of Publication Trends, Authors, and Journals

We also carried out a trend analysis of the number of publications, authors, and journals for the articles included in our analysis. For the trend analysis of the number of publications, we visualised each cluster’s annual trends and showed each cluster’s rise and fall over the past 20 years. The affiliations and countries of the top 20 authors, in terms of the number of publications, were listed to reveal the geographical distribution of the nurse staffing studies. The top 20 journals in terms of the number of publications were listed to identify which areas of medicine, nursing, and public health more likely covered the nurse staffing issue.

3. Results

3.1. Global Publication Trends from 2000 to 2022

Of the 2308 articles returned by the search strategy, 14 clusters were formed from 2167 papers based on citation relationships. In total, 141 articles were automatically dropped during the clustering process because they had no citation relationship with other articles. The overall number of papers published per year has been on the rise, starting with 26 per year in 2000 and gradually increasing to more than 100 per year in 2011 and more than 160 per year after 2020 (Figure 1a).

3.2. Analysis of Authors and Journals

Table 1 shows the top 20 most productive authors and their most recent affiliations and countries. Nurse staffing articles were mainly written by authors in the USA, UK, Canada, Australia, and Belgium. The top three researchers who published over 40 papers were Dr. Linda Aiken from the University of Pennsylvania, USA; Dr. Douglas M. Sloane from the University of Pennsylvania, USA; and Dr. Peter Griffiths from the University of Southampton, UK.
Table 2 shows the top 20 journals in terms of their number of publications related to nurse staffing. In descending order, the most publications were in the Journal of Nursing Administration, Journal of Nursing Management, and International Journal of Nursing Studies, which published over 80 related articles each. Some journals in the medical field, but not specifically the nursing field, were also ranked in the top 20, including Medical Care, Health Services Research, Health Affairs, BMJ Open, and the Journal of the American Geriatrics Society.

3.3. The Clustered Network Map of Co-Cited References

Out of the 14 clusters, the top five clusters comprised 97.5%. The 6th to 14th clusters, thus, only consisted of a total of 54 papers (2.5%). Therefore, in this paper, we discuss only the top five clusters, which included a level of papers that could be interpreted (Figure 2). Cluster #1 was titled “nurse outcome research in acute care hospitals”, Cluster #2 was titled “patient outcome research in acute care hospitals”, Cluster #3 was titled “nurse staffing mandate evaluation research”, Cluster #4 was titled “nursing home research”, and Cluster #5 was titled “school nurse research”. Over the past 20 years, the proportions of each cluster have not changed (Figure 1b).
Cluster #1, “nurse outcome research in acute care hospitals”, was the largest, with 771 articles. “ICU” (0.00171), “workload” (0.00075), “environment” (0.00061), “quality” (0.00050), and “outcome” (0.00045) were identified as representative key terms with high TF-ICFs (Table 3 and Appendix A Table A2). In this cluster, the papers mainly focused on the association between nurse staffing and nurses’ reporting on the quality of care and nurse outcomes (burnout, intent to stay in their role, satisfaction) in critical care or acute care hospitals (Table 3). The annual number of publications showed an increase overall, although there have been various increases and decreases. This cluster represents an annual share of 30–40% (Figure 1). The annual numbers were around 10 articles from 2000 to 2006, 20–40 articles from 2007 to 2012, and 40–70 articles from 2013 to 2022.
Cluster #2 was titled “patient outcome research in acute care hospitals” and comprised 614 articles. “Mortality” (0.00127), “patient outcome” (0.00104), “hospital” (0.00103), “fall” (0.00078), “surgical” (0.00066), and “readmission” (0.00062) were identified as key terms with high TF-ICFs (Table 3 and Appendix A Table A2). In this cluster, the papers mainly focused on the association between patient outcomes (mortality, length of hospital stay, fall, readmission) in acute care hospitals. Similar to Cluster #1, the annual number of publications in this cluster showed an increase overall and comprised an annual share of 20–37% (Figure 1). The annual numbers were 10–20 articles from 2000 to 2010; since 2011, the annual numbers almost doubled to around 40 articles.
Cluster #3, “nurse staffing mandate evaluation research”, comprised 537 articles. “Staffing level” (0.00225), “patient outcome” (0.00084), “registered nurse” (0.00064), “California” (0.00063), “policy” (0.00063), and “cost” (0.00058) were identified as key terms with high TF-ICFs (Table 3 and Appendix A Table A2). The papers in this cluster focused on the effect of nurse staffing mandates on patient and nurse outcomes, particularly on California’s mandate (Table 3). The annual number of publications in this cluster was around 20 articles from 2000 to 2022, representing a 14–35% annual share (Figure 1). There was an uptrend (n = 50 in 2015) in the four years from 2014 to 2018.
Cluster #4, “nursing home research”, comprised 138 articles. “Nursing home” (0.00728), “resident” (0.00433), “facility” (0.00154), “quality” (0.00127), “medicare” (0.00093), “rating” (0.00076), and “star” (0.00072) were identified as key terms with high TF-ICFs (Table 3 and Appendix A Table A2). In this cluster, the papers mainly focused on the association between nurse staffing and resident outcomes in line with policy changes in the Medicare system in the US, such as the Nursing Home Compare program and star ratings (Table 3). The annual number of publications ranged from 2 to 12, comprising approximately a 10% annual share. There were three uptrends from 2005 to 2008 (10 articles in 2006), 2012 to 2015 (8 articles in 2014 and 2015), and 2018 and 2022 (an average of 10 articles) (Figure 1).
Cluster #5, the “school nurse research” cluster, comprised 53 articles. “School nurse” (0.01518), “school” (0.00933), “student” (0.00395), “mental” (0.00238), “school nurse workload” (0.00231), “asthma” (0.00130), and “epinephrine” (0.00111) were identified as key terms with high IFICFs (Table 3 and Appendix A Table A2). In this cluster, the papers mainly focused on school nurse workload and the association between school nurse staffing and student outcomes in the US (Table 3). The annual number of publications was zero or one from 2000 to 2011; thereafter, fewer than five articles were published annually, except for 2008, when eight were published (Figure 1).
In terms of spatial location, Cluster #1 and Cluster #2, as well as Cluster #1 and Cluster #3, were found to be in close spatial distance and highly similar. Conversely, Clusters #4 and #5 were far from the other clusters and, thus, represented independent research topics (Figure 2).

4. Discussion

To the best of our knowledge, this study is the first to capture the landscape of articles on nurse staffing from 2000 to 2022 using a citation network analysis. In terms of the overall publication trends, we found that nurse staffing research has expanded, led by US and UK researchers. We also found that nurse staffing research can be divided into five main clusters according to the co-citation relationship: #1 “nurse outcome research in acute care hospitals”, #2 “patient outcomes research in acute care hospitals”, #3 “nurse staffing mandate evaluation research”, #4 “nursing home research”, and #5 “school nurse research”.

4.1. Overall Publication Trends and Leading Authors and Journals from 2000 to 2022

The overall trend of annual publications has been rising, indicating that the nurse staffing research field has been growing. However, it is suggested that more robust evidence on new adequate nurse staffing methods is needed, for instance, flexible nurse staffing methods beyond the use of ratios [49], shift work, working time [50,51], and nurse allocation methodology [52,53]. Thus, the theme of nurse staffing should continue to be a focus in the future.
Researchers from the US, the UK, Canada, Australia, and Belgium have led this field. This finding is consistent with the findings of a previous study focusing on the overall trend of high-impact nursing research papers, which are defined as papers ranked in the top 10% of citation frequency [30]. Among the top 20 contributors, the most prolific can be divided into the following three groups: the first includes Linda H. Aiken and Douglas M. Sloane at the University of Pennsylvania, US; the second includes Peter Griffiths and Jane Ball at the University of Southampton, UK; and the third includes Joan Spetz and Charlene Harrington at the University of California system, US. As for the first group, the University of Pennsylvania was also ranked number one in high-impact nursing research [54]; researchers at the University of Pennsylvania are also the major contributors to, and leaders in, the magnet hospital research [29] and the huge international nurse staffing research group “RN4CAST” was launched in 2007 [55,56]. As for the second group, Griffiths et al. (2016b) from the University of Southampton contributed to the adequate nurse staffing project at the National Institute for Health and Care Excellence in England, leading to the creation of nurse staffing policies in England [20,57]. The third group, Spetz et al. (2004, 2009) from the University of California, led an evaluation of the California nurse staffing mandate and nurse workforce research.
As for journals in which nurse staffing research is published, nursing administration journals are ranked highly. The International Journal of Nursing Studies, which has a broad scope across the nursing field, and other high-impact journals within the broader medical and health service research field also ranked highly, including Medical Care, Health Services Research, Health Affairs, BMJ Open, and the Journal of the American Geriatrics Society. Higher attention to nurse staffing in nursing administration, health policy, and health service research is indicated. However, the publication trends and leading authors and journals over the past two decades demonstrate that there is still limited knowledge and evidence on adequate nurse staffing from other countries, such as low-middle income counties and/or Asian regions with different nurse staffing regulation systems within their healthcare systems.

4.2. Cluster by Citation Network Analysis

Five main clusters in nurse staffing research over the past two decades were identified in this study: #1 “nurse outcome research in acute care hospitals”, #2 “patient outcome research in acute care hospitals”, #3 “nurse staffing mandate evaluation research”, #4 “nursing home research”, and #5 “school nurse research”. The first three clusters accounted for more than 80% of the total, and this share has not changed in the past 20 years.
The largest clusters were #1, “nurse outcome research in acute care hospitals”, and #2, “patient outcome research in acute care hospitals”. Although there was no small number of articles that examined both patient and nurse outcomes, those in Cluster #1, “nurse outcome research in acute care hospital”, were relatively more focused on the working environment and nurses’ turnover or retention in relation to the shortage in the nursing workforce.
Clusters #1, “nurse outcome research in acute care hospitals”, and #2, “patient outcome research in acute care hospitals”, were found to be spatially close, indicating the similarity in their context, with the only difference being whether they both focused on nurse outcomes or patient outcomes in nurse staffing research in acute care hospitals. Both clusters examine acute care hospital settings and reflect the trend of quality assurance in healthcare systems, mainly in the US, since the Institute of Medicine’s “To Err is Human” report. To assess the extent to which nursing personnel in acute care hospitals contribute to healthcare quality, patient safety, and a professional and safe work environment, the National Quality Forum developed 15 performance measures for nursing-sensitive care in 2004 [2]. Nursing sensitivity indicators included patient-centered outcomes, nursing-centered outcomes, and system-centered outcomes in nurse staffing and the Practice Environment Scale—Nursing Work Index (PES-NWI) [58]. Our findings indicate an uptrend in Cluster #1, “nurse outcome research in acute care hospitals”, after 2005, which the development of this nursing sensitivity outcome framework may explain. In addition, the uptrend in Cluster #2, “patient outcome research in acute care hospitals”, since 2011, may have been affected by the progress in the international research project RN4CAST [55,56]. Against the backdrop of worldwide nurse workforce shortages, RN4CAST was launched in 2007, coordinated by Walter Sermeus at Katholike Leuven, Belgium, and Linda Aiken at the University of Pennsylvania, to innovate forecasting methods by addressing the volume and quality of nursing staff as well as the quality of patient care. The project targeted 12 European countries, including the USA, Botswana, China, South Africa, and Chile. The findings from this project have been disseminated since 2011; this trend is shown in Figure 2c. Although the RN4CAST has a remarkable output, there are limitations in its cross-sectional design and manner of presenting the mean data, which do not consider the important differences in outcomes, staff characteristics, and care models [59].
Cluster #3 was titled “nurse staffing mandate evaluation research”. The most famous minimum nurse staffing ratio mandate is that of California, US. Since the minimum nurse staffing mandate was introduced in 2004 and became active in 2005, it has been empirically examined as a natural experiment [13,42]. Research articles that examined the expansion of mandates to other states then followed [40]. The spatial distance between Clusters #1, “nurse outcome research in acute care hospitals”, and #3, “nurse staffing mandate evaluation research”, was also close. This may reflect the context in which the nurse staffing mandate in California, US, developed as a California Nursing Association initiative to improve the working environment for nurses and patient safety. Further, there are also some research articles from Australia in which minimum nurse staffing ratio mandates have been introduced [16]. This trend is consistent with a previous systematic review that examined the evaluation of the nurse staffing methodology [60]. Currently, Germany [61], Japan [17], and Korea [18] have reformed nurse staffing mandates and regulations in the payment system, although evaluations of related policy changes are still minimal compared to those in the US and Australia. Although healthcare systems differ from country to country, it is important to share knowledge about the various safe nurse staffing regulations, including laws, payment systems, and guidelines for nursing staffing, and their effects worldwide to build a better nursing delivery system.
In Cluster #4, “nursing home research”, most articles were related to policy changes in Medicare Medicaid certification made by the Centers for Medicare Medicaid Service (CMS) in the US [62]. The Nursing Home Compare website was launched in 1998 for quality assurance in nursing homes and has since undergone several revisions. The Nursing Home Compare program presents the following five categories of information: inspection results, including deficiencies from Medicare; certification surveys and complaint investigations; facility characteristics; nursing home staffing levels; and quality measures, which provide information on the clinical and physical characteristics of a nursing home’s residents. This information is retrieved from the Online Survey Certification and Reporting (OSCAR) data and the Long-Term Care Minimum Data Set (MDS) Repository. In 2008, CMS launched the Five-Star Quality Rating System to help consumers, their families, and caregivers compare nursing homes more easily and identify areas about which they wanted to ask questions [63]. In addition, since 2002, nursing home pay-for-performance programs, based on the quality of the chronic care delivered and using financial incentives tied to Medicaid or Medicare payment, have been implemented in some states and CMSs [44]. Over the past two decades, approximately 10 papers have been published in this cluster annually. Understanding nurse staffing and its association with resident and nurse outcomes in long-term care settings is necessary. In addition, the importance of research in long-term care settings is even greater for both Asian countries with already super-aged societies and Asian and African countries that could become aged societies in the relatively near future.
Cluster #5, “school nurse research”, was a relatively newly developed area of research. The annual number of papers was over five for the first time in 2018, and the total amount of research in this cluster is somewhat limited compared to research in acute care hospital settings. School nurses take on the role of case managers, bringing providers, families, and schools together to support students’ health; as a result, better attendance and academic success are gained [64,65]. Some states in the US recommend one school nurse for every 750 students in the healthy student population, a ratio of 1:225 for student populations requiring daily professional nursing services, a ratio of 1:125 for student populations with complex healthcare needs, and a ratio of 1:1 for individual students requiring daily, continuous professional nursing services [66]. However, the National Association of School Nurses stated that the workload of school nurses has been expanding in line with an increase in children with mental health issues and those requiring special medical treatment [66]. More evidence is necessary to ensure adequate and safe nurse staffing in school settings.

4.3. Limitations

This study has some limitations. The first relates to the search database. The WOS database is widely used in bibliometric analysis; however, we used PubMed because we could only access the current trends via WOS from 2000 to 2011 due to funding limitations. However, using PubMed has some benefits. For example, PubMed has the advantages of being the best database in the medical field, having an optimal update frequency, and including early online articles [67]. Future studies should extend the search to WOS and other databases. Second, we could not include articles in which the title, abstract, and text were in a language other than English. Thus, we might have overestimated the geographic bias of the evidence on nurse staffing. Nevertheless, this citation analysis provides a more comprehensive picture of nurse staffing research in that, even if the text was in another language, it was included as long as some parts of the article, such as the title and abstract, were in English. Thus, this study included studies from the literature that have been excluded from traditional literature reviews. Third, since the method used in this study analyzes direct citation networks, the results may be biased due to the issue that papers already cited in other works in the literature are more likely to be cited in new papers. In recent years, it has been noted that evaluating researchers using the citation matrix is invalid as it depends on citation and publication counts [63]. Alternative indexes considering co-author contributions and publication age beyond the sole publication count have been developed [68]. Fourth, although we revealed a landscape of over 2000 nurse staffing articles from the viewpoint of the citation network, we could not evaluate the research quality of each article in the same manner as a systematic review. However, it is crucial to capture the latest research trends in real time from the rapidly growing academic literature. We believe that sharing the results of our analysis with the nursing community, including researchers, could provide objective evidence to help determine this community’s future direction.

5. Conclusions

Using citation analysis, nurse staffing research over the past two decades formed five major clusters, depending on the study setting and outcomes focused on. This landscape of over 2000 nurse staffing articles revealed that evidence regarding long-term care settings and/or those from other geographic areas is still small compared to those in acute care settings from the US or UK. To ensure the safety of patients and nurses in all practice settings and locations, diverse geographic and setting knowledge is essential.

Author Contributions

Conceptualization, N.M., S.O., M.M., K.H. and M.K.; methodology, N.M., S.O. and M.O.; software, N.M., S.O. and M.O.; formal analysis, N.M., S.O. and M.O.; writing—original draft preparation, N.M.; writing—review and editing, N.M., S.O., M.O., M.M., K.H., I.S. and M.K; visualization, N.M. and S.O.; supervision, M.M., K.H., I.S. and M.K; funding acquisition, N.M., K.H. and I.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Health, Labour and Welfare Program, grant number (21IA1002), and a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan (No. 20H03972 and No.21K2118), and the New Energy and Industrial Technology Development Organization (NEDO) in Japan (No. JPNP20006). The funders had no role in the study design, data collection and analysis, decision to publish, or the preparation of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analysed in this study. All data are provided in the article. Further data can be provided on request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Healthcare 11 03050 g0a1
Figure A1. Overview of the citation analysis procedure.
Figure A1. Overview of the citation analysis procedure.
Healthcare 11 03050 g0a1
Table A1. Search strategy.
Table A1. Search strategy.
Number of Articles That Met the Search Query
“nurse staffing”26,194
“nurse workload”8113
“nurse workforce”18,801
“nurses to patients”163,885
“nurse to patients”163,885
“nurse to patient”163,885
“nurses-to-patients”163,885
“nurse-to-patients”163,885
“nurse-to-patient”414
“patients to nurses”163,885
“patient to nurses”163,885
“patient to nurse”163,885
“patients-to-nurses”163,885
“patient-to-nurses”163,885
“patient-to-nurse”171
“nurses to beds”2357
“nurse to beds”2357
“nurse to bed”7235
“nurses-to-beds”7235
“nurse-to-beds”7235
“nurse-to-bed”39
number of nurses29,725
“nurse staffing” [Title/Abstract]1752
“nurse workload” [Title/Abstract]147
“nurse workforce” [Title/Abstract]280
“nurses to patients” [Title/Abstract]0
“nurse to patients” [Title/Abstract]0
“nurse to patient” [Title/Abstract]415
“nurses-to-patients” [Title/Abstract]0
“nurse-to-patients” [Title/Abstract]0
“nurse-to-patient” [Title/Abstract]415
“patients to nurses” [Title/Abstract]0
“patient to nurses” [Title/Abstract]0
“patient to nurse” [Title/Abstract]171
“patients-to-nurses” [Title/Abstract]0
“patient-to-nurses” [Title/Abstract]0
“patient-to-nurse” [Title/Abstract]171
“nurses to beds” [Title/Abstract]0
“nurse to beds” [Title/Abstract]0
“nurse to bed” [Title/Abstract]39
“nurses-to-beds” [Title/Abstract]0
“nurse-to-beds” [Title/Abstract]0
“nurse-to-bed” [Title/Abstract]39
“nurse staffing” [Title/Abstract] OR “nurse workload” [Title/Abstract] OR “nurse workforce” [Title/Abstract] OR “nurse to patient” [Title/Abstract] OR “patient to nurse” [Title/Abstract] OR “nurse to bed” [Title/Abstract]2563
Table A2. Details of the TF-ICF score of each keyword in the five main clusters.
Table A2. Details of the TF-ICF score of each keyword in the five main clusters.
Cluster #1 Cluster #2 Cluster #3
TERMTFICFTCCCTFICF TERMTFICFTCCCTFICF TERMTFICFTCCCTFICF
icu0.00171 65240.00314 0.54407 mortality0.00127 59160.00344 0.36798 staffing level0.00225 56360.00611 0.36798
nursing0.00130 2575110.01239 0.10474 patient outcome0.00104 39850.00232 0.44716 level0.00219 83080.00901 0.24304
intensive care0.00086 59770.00287 0.30103 hospital0.00103 2654120.01545 0.06695 nurse staffing level0.00171 35250.00382 0.44716
workforce0.00085 58470.00281 0.30103 level0.00089 62780.00365 0.24304 nurse staffing0.00116 1594120.01730 0.06695
nursing care0.00084 58270.00280 0.30103 patient0.00080 4249130.02473 0.03218 staffing0.00092 2626130.02850 0.03218
workload0.00075 51770.00249 0.30103 icu0.00079 25040.00145 0.54407 patient outcome0.00084 17450.00189 0.44716
intensive care unit0.00073 41360.00199 0.36798 fall0.00078 24640.00143 0.54407 outcome0.00078 493100.00535 0.14613
care unit0.00069 47970.00231 0.30103 outcome0.00077 902100.00525 0.14613 relationship0.00077 23770.00257 0.30103
unit0.00066 942100.00453 0.14613 nursing0.00069 1135110.00661 0.10474 nursing0.00076 665110.00722 0.10474
level0.00066 56280.00270 0.24304 rate0.00067 47580.00276 0.24304 registered0.00065 19970.00216 0.30103
work0.00063 67990.00327 0.19189 surgical0.00066 25250.00147 0.44716 hospital0.00065 889120.00965 0.06695
job0.00062 42970.00206 0.30103 readmission0.00062 23750.00138 0.44716 registered nurse0.00064 19570.00212 0.30103
environment0.00061 52280.00251 0.24304 unit0.00060 700100.00407 0.14613 california0.00063 10740.00116 0.54407
critical care0.00058 22140.00106 0.54407 cost0.00056 31770.00184 0.30103 policy0.00063 15760.00170 0.36798
intensive0.00057 62290.00299 0.19189 data0.00055 646100.00376 0.14613 data0.00060 377100.00409 0.14613
work environment0.00056 26150.00126 0.44716 ratio0.00054 887110.00516 0.10474 mortality0.00059 14860.00161 0.36798
missed0.00056 25950.00125 0.44716 effect0.00052 29870.00173 0.30103 cost0.00058 17870.00193 0.30103
turnover0.00055 21140.00102 0.54407 ulcer0.00052 16440.00095 0.54407 staffing and patient0.00053 7330.00079 0.66901
care0.00051 3275130.01576 0.03218 associated0.00051 600100.00349 0.14613 effect0.00051 15670.00169 0.30103
quality0.00050 718100.00346 0.14613 surgery0.00051 19550.00113 0.44716 quality0.00050 317100.00344 0.14613
critical0.00049 27960.00134 0.36798 complication0.00050 15840.00092 0.54407 nurse staffing and patient0.00049 6830.00074 0.66901
infant0.00046 17540.00084 0.54407 workload0.00050 28370.00165 0.30103 unit0.00048 302100.00328 0.14613
outcome0.00045 638100.00307 0.14613 risk0.00047 33480.00194 0.24304 research0.00045 21890.00237 0.19189
job satisfaction0.00044 16940.00081 0.54407 pressure ulcer0.00047 14940.00087 0.54407 skill mix0.00045 9350.00101 0.44716
data0.00043 609100.00293 0.14613 odds0.00046 21460.00125 0.36798 association0.00043 20790.00225 0.19189
satisfaction0.00042 29170.00140 0.30103 hospital mortality0.00045 11630.00068 0.66901 evidence0.00043 20590.00223 0.19189
registered0.00042 28970.00139 0.30103 lower0.00045 25770.00150 0.30103 safety0.00042 15880.00171 0.24304
mortality0.00041 23460.00113 0.36798 staffing level0.00044 20560.00119 0.36798 mix0.00041 12670.00137 0.30103
decision0.00035 16350.00078 0.44716 patient ratio0.00039 35290.00205 0.19189 model0.00035 16990.00183 0.19189
Cluster #4 Cluster #5
TERMTFICFTCCCTFICF TERMTFICFTCCCTFICF
nursing home0.00728 64750.01627 0.44716 school nurse0.01518 16410.01324 1.14613
resident0.00433 38550.00968 0.44716 school0.00933 31460.02535 0.36798
home0.00418 68480.01720 0.24304 student0.00395 9040.00727 0.54407
nursing0.00223 845110.02125 0.10474 school nursing0.00305 3310.00266 1.14613
facility0.00154 25280.00634 0.24304 mental0.00238 6650.00533 0.44716
quality0.00127 345100.00868 0.14613 school nurse workload0.00231 2510.00202 1.14613
home resident0.00116 6930.00174 0.66901 mental health0.00206 5750.00460 0.44716
nursing home resident0.00116 6930.00174 0.66901 pmh0.00176 1910.00153 1.14613
medicare0.00105 7740.00194 0.54407 health0.00169 200110.01615 0.10474
level0.00105 17180.00430 0.24304 workforce0.00158 6570.00525 0.30103
deficiency0.00096 8550.00214 0.44716 school health0.00157 1710.00137 1.14613
chain0.00094 5630.00141 0.66901 workload0.00146 6070.00484 0.30103
medicaid0.00093 8350.00209 0.44716 asthma0.00130 2430.00194 0.66901
nursing facility0.00089 5330.00133 0.66901 epinephrine0.00111 1210.00097 1.14613
life care0.00087 4120.00103 0.84510 psychiatric mental0.00102 1110.00089 1.14613
registered0.00083 11070.00277 0.30103 psychiatric mental health0.00102 1110.00089 1.14613
registered nurse0.00081 10770.00269 0.30103 school setting0.00102 1110.00089 1.14613
rating0.00076 6850.00171 0.44716 adolescent0.00096 1420.00113 0.84510
star0.00072 4330.00108 0.66901 school district0.00093 1010.00081 1.14613
hprd0.00072 3420.00086 0.84510 school nurse staffing0.00093 1010.00081 1.14613
staffing level0.00069 7560.00189 0.36798 public school0.00093 1010.00081 1.14613
home quality0.00066 3120.00078 0.84510 school nurse workforce0.00093 1010.00081 1.14613
nursing home quality0.00066 3120.00078 0.84510 anaphylaxis0.00093 1010.00081 1.14613
profit0.00062 4540.00113 0.54407 allergy0.00093 1010.00081 1.14613
state0.00060 12590.00314 0.19189 hop0.00093 1010.00081 1.14613
case0.00058 7770.00194 0.30103 psychiatric0.00083 2350.00186 0.44716
effect0.00056 7470.00186 0.30103 policy0.00077 2660.00210 0.36798
relationship0.00055 7370.00184 0.30103 academic outcome0.00074 810.00065 1.14613
life0.00045 6070.00151 0.30103 nursing0.00068 80110.00646 0.10474
CC: number of clusters including the term, COVID: coronavirus disease, HPRD: hours per resident day, ICF: log [1 − (total number of clusters/CC)], ICU: intensive care unit, PMH: Psychiatric Mental Health, TC: term (keyword) counts in the cluster, TF-ICF = TF × ICF, WISN: workload indicators of staffing needs.

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Healthcare 11 03050 g001
Figure 1. Trends in the number of articles published from 2000 to 2022 (n = 2167). Cluster #1 was titled “nurse outcome research in acute care hospitals”, Cluster #2 was titled “patient outcome research in acute care hospitals”, Cluster #3 was titled “nurse staffing mandate evaluation research”, Cluster #4 was titled “nursing home research”, Cluster #5 was titled “school nurse research”, and Clusters #6–14 are clusters that are small in number and were not included in the top five main clusters. (a) Trends in the number of publications from 2000 to 2022; (b) Trends in the proportions of each cluster from 2000 to 2022.
Figure 1. Trends in the number of articles published from 2000 to 2022 (n = 2167). Cluster #1 was titled “nurse outcome research in acute care hospitals”, Cluster #2 was titled “patient outcome research in acute care hospitals”, Cluster #3 was titled “nurse staffing mandate evaluation research”, Cluster #4 was titled “nursing home research”, Cluster #5 was titled “school nurse research”, and Clusters #6–14 are clusters that are small in number and were not included in the top five main clusters. (a) Trends in the number of publications from 2000 to 2022; (b) Trends in the proportions of each cluster from 2000 to 2022.
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Figure 2. Visualization of the citation network of the top 5 clusters. Cluster #1 was titled “nurse outcome research in acute care hospitals”, Cluster #2 was titled “patient outcome research in acute care hospitals”, Cluster #3 was titled “nurse staffing mandate evaluation research”, Cluster #4 was titled “nursing home research”, and Cluster #5 was titled “school nurse research”. The size of the clusters indicates the number of papers included, and the spatial distance between clusters indicates the similarity of the content. Clusters that are far apart in spatial distance indicate independent research topics with a limited citation relationship.
Figure 2. Visualization of the citation network of the top 5 clusters. Cluster #1 was titled “nurse outcome research in acute care hospitals”, Cluster #2 was titled “patient outcome research in acute care hospitals”, Cluster #3 was titled “nurse staffing mandate evaluation research”, Cluster #4 was titled “nursing home research”, and Cluster #5 was titled “school nurse research”. The size of the clusters indicates the number of papers included, and the spatial distance between clusters indicates the similarity of the content. Clusters that are far apart in spatial distance indicate independent research topics with a limited citation relationship.
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Table 1. Affiliations and countries of the top 20 authors.
Table 1. Affiliations and countries of the top 20 authors.
AuthorNumber of ArticlesAffiliationCountry
Linda H. Aiken95University of PennsylvaniaUSAHealthcare 11 03050 i001
Douglas M. Sloane51University of PennsylvaniaUSAHealthcare 11 03050 i001
Peter Griffiths44University of SouthamptonUKHealthcare 11 03050 i002
Matthew D. McHugh36University of PennsylvaniaUSAHealthcare 11 03050 i001
Joanne Spetz27University of California, San FranciscoUSAHealthcare 11 03050 i001
Sean P. Clarke27McGill University, QuebecCanadaHealthcare 11 03050 i003
Charlene Harrington25University of California, San FranciscoUSAHealthcare 11 03050 i001
Walter Sermeus23KU Leuven-University of LeuvenBelgiumHealthcare 11 03050 i004
Eileen T. Lake22University of PennsylvaniaUSAHealthcare 11 03050 i001
Christine Duffield21University of Technology Sydney/Edith Cowan UniversityAustraliaHealthcare 11 03050 i005
Jeannie P. Cimiotti20Emory University, GeorgiaUSAHealthcare 11 03050 i001
Kathleen Rice Simpson19Mercy Hospital, MissouriUSAHealthcare 11 03050 i001
Barbara A. Mark19University of North Carolina at Chapel HillUSAHealthcare 11 03050 i001
Jane Ball18University of SouthamptonUKHealthcare 11 03050 i002
Peter I. Buerhaus18Montana State UniversityUSAHealthcare 11 03050 i001
Jack Needleman18University of California, Los Angeles, San FranciscoUSAHealthcare 11 03050 i001
Koen Van den Heede17KU Leuven-University of LeuvenBelgiumHealthcare 11 03050 i004
David W. Harless16Virginia Commonwealth UniversityUSAHealthcare 11 03050 i001
Vincent S. Staggs15University of Missouri-Kansas City, MissouriUSAHealthcare 11 03050 i001
Luk Bruyneel13KU Leuven-University of LeuvenBelgiumHealthcare 11 03050 i004
Anne Marie Rafferty13King’s College LondonUKHealthcare 11 03050 i002
The most recent literature in the PubMed search results was used to identify each author’s affiliation. UK; United Kingdom, USA; United States of America.
Table 2. Titles and Journal Impact FactorTM of top 20 journals.
Table 2. Titles and Journal Impact FactorTM of top 20 journals.
Journal TitleNumber of ArticlesJournal Impact Factor in 2021
Journal of Nursing Administration881.806
Journal of Nursing Management854.682
International Journal of Nursing Studies816.612
Journal of Advanced Nursing523.057
Nursing Economics491.193
Medical Care473.178
Health Services Research453.734
Journal of Clinical Nursing434.423
Nursing Standard43no data
Journal of Nursing Scholarship333.928
Policy, Politics & Nursing Practice33no data
Journal of Nursing Care Quality291.728
Health Affairs299.048
BMJ Open233.007
International Nursing Review233.384
American Journal of Nursing222.577
MCN: The American Journal of Maternal-Child Nursing211.753
Nursing Research192.364
Nursing Outlook193.315
Nursing Times19no data
Journal of the American Geriatrics Society197.538
Modern Healthcare19no data
Journal Impact FactorTM was taken from the Web of Science (Clarivate).
Table 3. Representative keywords and papers in the top five clusters.
Table 3. Representative keywords and papers in the top five clusters.
Cluster NameKeywords (TF-ICF)Examples of Included Papers
#1 “nurse outcome research in acute care hospital”ICU (0.00171), workforce (0.00085), nursing care (0.00084), workload (0.00075), work environment (0.00056), missed (0.00056)[3,37]
#2 “patient outcome research in acute care hospitals”mortality (0.00127), patient outcome (0.00104), hospital (0.00103), patient (0.00080), fall (0.00078), outcome (0.00077)[4,38,39]
#3 “nurse staffing mandate evaluation research”staffing level (0.00225), patient outcome (0.00084), hospital (0.00065), registered nurse (0.00064), California (0.00063), policy (0.00063)[40,41,42]
#4 “nursing home research”nursing home (0.00728), resident (0.00433), medicare (0.00105), deficiency (0.00096), Medicaid (0.00093), nursing facility (0.00089)[43,44,45]
#5 “school nurse research”school nurse (0.01518), school (0.00933), student (0.00395), mental (0.00238), school nurse workload (0.00231), asthma (0.00130)[46,47,48]
TF-ICF: term frequency-inverse cluster frequency.
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Morioka, N.; Ochi, M.; Okubo, S.; Moriwaki, M.; Hayashida, K.; Sakata, I.; Kashiwagi, M. Citation Network Analysis of Nurse Staffing Research from the Past Two Decades: 2000–2022. Healthcare 2023, 11, 3050. https://doi.org/10.3390/healthcare11233050

AMA Style

Morioka N, Ochi M, Okubo S, Moriwaki M, Hayashida K, Sakata I, Kashiwagi M. Citation Network Analysis of Nurse Staffing Research from the Past Two Decades: 2000–2022. Healthcare. 2023; 11(23):3050. https://doi.org/10.3390/healthcare11233050

Chicago/Turabian Style

Morioka, Noriko, Masanao Ochi, Suguru Okubo, Mutsuko Moriwaki, Kenshi Hayashida, Ichiro Sakata, and Masayo Kashiwagi. 2023. "Citation Network Analysis of Nurse Staffing Research from the Past Two Decades: 2000–2022" Healthcare 11, no. 23: 3050. https://doi.org/10.3390/healthcare11233050

APA Style

Morioka, N., Ochi, M., Okubo, S., Moriwaki, M., Hayashida, K., Sakata, I., & Kashiwagi, M. (2023). Citation Network Analysis of Nurse Staffing Research from the Past Two Decades: 2000–2022. Healthcare, 11(23), 3050. https://doi.org/10.3390/healthcare11233050

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