Factors influencing healthcare workers’ performance before and after the coronavirus disease 2019 pandemic: A bibliometric analysis with supplementary comparative analysis

Abstract

BACKGROUND:

The performance of healthcare workers directly impacts patient safety and treatment outcomes. This was particularly evident during the coronavirus disease 2019 (COVID-19) pandemic.

OBJECTIVE:

This study aimed to analyze research trends on factors influencing work performance among healthcare workers through bibliometric analysis and conduct a comparative analysis from macro and micro perspectives before and after the COVID-19 pandemic to complement the existing research.

METHODS:

This study involved a bibliometric analysis of 1408 articles related to work performance in the healthcare field published between 2010 and 2023, using the Web of Science, Scopus, and PubMed databases, and 37 articles were selected to determine the factors influencing work performance.

RESULTS:

By conducting a bibliometric analysis of the articles based on country, institution, journal, co-cited references, and keywords, this study identified a significant growth trend regarding the factors influencing work performance in the healthcare field, and research hotspots shifted from organizational factors like standard towards psychological factors such as burnout, anxiety, and depression following the outbreak of the COVID-19 pandemic. Subsequently, this study extracted 10 micro-level and 9 macro-level influencing factors from the selected articles for supplementary analysis. Furthermore, this study conducted a comparative analysis of the impact of these factors on work performance before and after the COVID-19 pandemic.

CONCLUSIONS:

This study addressed the limitations of previous studies regarding incomplete extraction of factors influencing work performance and unclear comparisons of parameters before and after the COVID-19 pandemic. The findings provide insights and guidance for improving the performance of healthcare workers.

Keywords

Healthcare workers work performance influencing factors COVID-19 pandemic

1 Introduction

Healthcare operations are characterized by urgency, variability, individual differences, invasiveness, and multidisciplinary collaboration; they are closely related to patient safety and involve high workloads and high-precision job requirements [1, 2]. The performance of healthcare workers impacts patient safety and treatment outcomes, and a deficiency in their performance can cause serious medical errors [3, 4]. Since the publication of “To Err Is Human” in 2000, medical errors have received widespread attention, and patient safety and work performance of healthcare workers have been emphasized [5]. Medical errors, which are primarily caused by human factors, refer to deviations between the expected and actual behaviors in healthcare operations [6] and are the third leading cause of death in the United States [7]. In the United Kingdom, 237 million medication errors are estimated yearly, with 66 million being potentially clinically significant. The number of deaths caused by such errors exceeds approximately 1,700 annually [3]. Medical errors can lead to a decline in work performance; therefore, it is necessary to identify and enhance the factors influencing healthcare workers’ performance to improve the work environment and overall performance.

The specificity of healthcare operations places a heavy workload, huge responsibility, and high pressure on healthcare workers [8]. Approximately 18% of nurses are forced to quit their jobs due to heavy workloads [9]; for example, in China, 46% of the nurses work more than 11 h per day, while 35% work more than 6 days per week [10]. Improving work performance ensures patient safety [11]; therefore, systematically exploring the factors that influence the performance of healthcare workers is necessary. Since the outbreak of the global novel coronavirus in December 2019 (coronavirus disease 2019 [COVID-19]), the psychological pressure and workload of healthcare workers have dramatically increased [4, 12]. Although the World Health Organization declared on May 5, 2023, that the novel coronavirus no longer constitutes an international emergency of public health concern, healthcare workers have experienced more intense work pressure and workloads throughout the 3-year duration of the COVID-19 pandemic [13].

The work performance of healthcare workers refers to the outcomes and effects achieved by healthcare workers in their work processes [14]. Kagan et al. conducted a questionnaire survey with 87 registered nurses working in a Klebsiella pneumoniae isolation ward and identified mastery of professional knowledge, self-perceived professional competence, and work experience in the isolation ward as the primary influencing factors of work performance [15]. Meanwhile, Kalmbach et al. conducted a questionnaire survey of 1,215 medical interns to investigate the relationship between sleep duration, working hours, emotions, and work performance. They found that sleep disturbances or brief sleep due to internship obligations and excessive working hours can affect the interns’ emotional state, increase their risk of depression and medical errors, and reduce their work performance [16]. Additionally, Chao et al. conducted a questionnaire survey of 344 healthcare workers in a rural hospital in Taiwan to explore the relationship between job stress, job satisfaction, work performance, and intention to quit and found that job stress positively impacted work performance but negatively influenced job satisfaction. Job stress and satisfaction were the two primary factors influencing work performance and intention to quit [17]. These studies have primarily used questionnaire surveys to identify the factors influencing the work performance of healthcare workers; however, they did not consider complete coverage of healthcare worker populations and a mix of macro- and micro-level factors.

The performance of healthcare workers during the COVID-19 pandemic remains a research hotspot. Sun et al. conducted a survey of 669 healthcare workers in Pakistan using a structured questionnaire and reported that stress, depression, and anxiety during COVID-19 could exacerbate fatigue and adversely affect the mental health level of healthcare workers, thereby affecting their work performance. Furthermore, Sarikose et al. used a questionnaire survey to explore the relationship between the nursing quality foundation and the level of nursing work performance among 370 nurses working in a private hospital and two university hospitals in Turkey. They identified factors, including the work environment, staffing, adequacy of resources, superior competence, nurses’ capabilities, receiving support, and colleague solidarity, as significant factors influencing work performance [18]. The factors affecting work performance proposed in these studies accurately reflect the characteristics of the medical work environment during a pandemic; however, comparing these factors with those observed during the non-pandemic is essential for a better understanding of the changes in factors influencing work performance before and after the COVID-19 pandemic. Therefore, it is necessary to conduct a bibliometric overview of the research trends on influencing factors of work performance among healthcare workers and further explore the changes in influencing factors of work performance before and after the COVID-19 pandemic from macro and micro perspectives through supplementary comparative analysis.

Overall, the current research on factors influencing healthcare workers’ work performance faces the following challenges: i) the identification and categorization of factors affecting work performance are inadequate; ii) the influencing factors of operation performance are a combination of macro and micro factors; and iii) recently, research on factors affecting operational performance during COVID-19 has become one of the concerns; however, what are the differences between this time and non-COVID-19 periods? Hence, it is imperative to employ bibliometric analysis to survey the research dynamics of factors influencing healthcare workers’ work performance and undertake a supplementary comparative analysis to investigate the changes in these influencing factors from macro and micro perspectives, particularly before and after the COVID-19 pandemic.

2 Methods

2.1 Bibliometric analysis

2.1.1 Bibliometric analysis introduction

Bibliometrics first emerged in the early 20th century and was defined in 1969 as “the application of mathematics and statistical methods to books and other media of communication.” Since then, it has been widely used in literature analysis[19]. Additionally, bibliometrics is a tool for both quantitative and qualitative analysis of research hotspots and trends in specific fields [20 –22]. It achieves this by collecting information, such as authors, keywords, countries, journals, institutions, and references from literature, using graphical and visual visualization effects to reveal the inherent connections between literature items, thereby providing insights into the development of the target field [23, 24]. Bibliometrics is also frequently applied to explore research dynamics in the medical field [25, 26].

We can reveal associations, citation relationships, and development trends among literature items using bibliometrics. Based on this quantitative and objective analytical approach, we can accurately extract factors related to healthcare workers’ work performance for further in-depth analysis.

2.1.2 Bibliometric analysis software

Currently, various software tools are available for bibliometric analysis, such as Citespace, Vosviewer, Hastro, BibExcel, and CitNetExplorer. In this study, we primarily used Citespace and Vosviewer. These two software tools are widely employed by international scholars in bibliometric analysis due to their advantages in performance and graphical aesthetics [27]. CiteSpace employs set-theoretic data standardization methods to measure similarity between knowledge units and provides a clear depiction of the evolution and historical span of information in a particular field over time. Therefore, this enables the visualization of information related to authors, countries, and citations. However, Vosviewer utilizes probabilistic data standardization methods and provides various visualization views in fields, such as keywords and institutions, including network cluster view, overlay visualization, and density visualization. Its strengths lie in producing visually appealing graphics and simplifying mapping [27 –29].

In Citespace-generated visualizations, node size represents the number of published articles; larger nodes indicate a higher volume of publications. The thickness of connections between nodes signifies the strength of collaboration, with thicker connections indicating closer collaboration. Additionally, node color reflects the publication year of the articles. Node centrality represents the connecting ability of each node; higher centrality indicates that the node serves as a transitional key point connecting multiple groups. Cluster colors denote different clusters, with larger clusters indicating a higher number of keywords within them. In Vosviewer-generated density views, node color indicates the density of surrounding elements, with higher and lower densities closer to red and blue, respectively. Density is determined by the quantity and importance of surrounding elements. In Vosviewer-generated overlay visualizations, node size represents the frequency of appearance in research, with larger nodes indicating higher research prominence in the field. Node color represents different years, mapped according to the average publication year of the elements [29].

2.1.3 Bibliometric analysis method

In this study, bibliometric analysis was conducted based on a dataset comprising 1408 identified publications related to healthcare workers’ work performance. Three main analyses were performed. First, descriptive analysis was performed to gain an overall understanding of the publication patterns in the research field by examining the countries, institutions, and journals of the literature. Second, co-citation analysis was conducted to identify influential articles in the field by analyzing co-citation and burst references, showing how research hotspots have evolved over time. This analysis helps identify key or frontier articles in the field, reveal recent research trends, and forecast future research directions. Finally, keyword analysis was performed to provide a concrete analysis of research hotspots in the field. Keywords are highly refined terms used by authors to represent their research, typically including information about the research subject, perspective, and methods. We examined the thematic characteristics of different keyword clusters using keyword clustering analysis, identifying popular topics. By analyzing keywords frequently co-occurring, we can reflect longstanding research hotspots in the field and help retrieve the distribution of research hotspots within the domain. Furthermore, by comparing co-citation networks of keywords from different years, we can uncover the knowledge structure related to work performance in the healthcare field and potential future research trends.

2.1.4 Search strategy

This study aimed to review, extract, and summarize the factors influencing work performance in the healthcare field. When conducting bibliometric analysis, this study selected Web of Science, Scopus, and PubMed as search databases. In determining the search keywords, this study considered the following two aspects: the research participants and objectives. The research participants of this study were healthcare workers, and the research objective was to identify the factors influencing work performance. Therefore, this study primarily focused on the following three phrases: healthcare workers, work performance, and influencing factors.

When exporting the results, each search item was exported in “.txt” format with “full records and citations” to collect as much additional information as possible. Regarding the selection of publication years for the literature search, considering that the global spread of the COVID-19 pandemic began in 2020 and has been ongoing for approximately 3 years, most articles related to the work performance of healthcare workers during this period revolve around the pandemic. Thus, we selected articles from the non-pandemic period of 2010–2019. Additionally, we set the search scope from 2010 to 2023, covering approximately 13 years. The specific search strategy is outlined in Table 1 as follows:

Table 1
Summary of the search strategy

Category Specific Standard Requirements

Research database Web of science core collection “Scopus” PubMed

Searching period 2010-2023

Language “English”

Searching keywords (“nurse” OR “health care workers” OR “healthcare workers” OR “health care providers” OR “primary care providers” OR “frontline health workers” OR “care providers” OR “medical workers” OR “medical”) AND (“job performance” OR “employee’s performance “OR” work performance”) AND (factor)

Sample size 1476

Category Specific Standard Requirements

Category	Specific Standard Requirements
Research database	Web of science core collection “Scopus” PubMed
Searching period	2010-2023
Language	“English”
Searching keywords	(“nurse” OR “health care workers” OR “healthcare workers” OR “health care providers” OR “primary care providers” OR “frontline health workers” OR “care providers” OR “medical workers” OR “medical”) AND (“job performance” OR “employee’s performance “OR” work performance”) AND (factor)
Sample size	1476
Category	Specific Standard Requirements

2.2 Selection criteria

The process of filtering the literature in this study comprised two phases.

In the first phase, to investigate the research dynamics of influencing factors on the work performance of healthcare workers, this study initially selected Web of Science, Scopus, and PubMed as retrieval databases. This study conducted a literature search centered on the phrases “healthcare workers,” “work performance,” and “influencing factors,” as well as related keywords. After identifying and removing duplicate and irrelevant articles, this study proceeded with bibliometric analysis.

In the second phase, to compare the influencing factors of work performance before and after the COVID-19 pandemic from both macro and micro perspectives, this study further extracted articles from the literature obtained in the first stage for supplementary comparative analysis. The author team conducted searches using the Web of Science core database, utilizing Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) as citation indexes.

Subsequently, the team independently reviewed the abstract, keywords, and main text of these papers. Articles were excluded if they only focused solely on the work performance of the medical profession rather than that of healthcare workers, did not explicitly present the specific content of influencing factors, or only mentioned influencing factors without specifying their specific impact on work performance. Additionally, to further clarify the literature retrieval process and purpose of this study, we have included a retrieval flowchart (as shown in Fig. 1 below) for supplementary explanation. In this flowchart, we have annotated the two stages of literature retrieval and provided a description of the purposes and contents of each stage.

Fig. 1

Literature retrieval process.

3 Results

3.1 Literature information

Overall, 1476 articles were collected. During the first stage, 68 duplicate and irrelevant articles were excluded, while the remaining 1408, which originated from 4744 institutions in 116 countries, had a total of 6650 authors, and were published in 854 different journals, were used for bibliometric analysis. These articles cited a total of 10894 references from 4149 different journals and were authored by 8506 individuals. The distribution of publication years for these 1408 articles is shown in Fig. 2.

Fig. 2

Time trend of the publications.

The number of publications in this field showed an overall increasing trend over the years (with some years experiencing a slight decline). There has been a significant increase in the number of publications, particularly after 2020. This surge reflects the growing attention from scholars, especially since 2020, when the COVID-19 pandemic emerged, making it a prominent topic of interest in the healthcare workforce domain.

3.2 Descriptive statistics

3.2.1 Bibliometric analysis of the country

Visualization was conducted on countries with more than thirty-eight publications using CiteSpace, as shown in Fig. 3.

Fig. 3

Country analysis.

From the results of the visualizations above, the United States had the highest number of publications, followed by China; scholars from China and Canada publish articles almost every year, while Kore an scholars have published a larger number of articles in the last 3 years. Additionally, scholars from the United States had the most frequent collaborations with scholars from other countries.

This study calculated the average citation count for the top 12 countries regarding publication volume to further analyze the highly productive countries in this field. The specific data are presented in Table 2.

Table 2

Top 12 countries contributing to publications

Num.	Country	Publications	Citations	Average
1	United States	406	4796	11.81
2	China	106	632	5.96
3	United Kingdom	96	161	1.68
4	Canada	81	252	3.11
5	Australia	77	371	4.82
6	Netherlands	67	655	9.78
7	Germany	60	267	4.45
8	South Korea	60	216	3.60
9	Iran	59	101	1.71
10	Italy	51	140	2.75
11	Spain	40	187	4.68
12	Japan	38	174	4.58

The United States has made the most significant research contribution in this field, with a total of 406 published articles and the highest number of citations, reaching 4796 and ranked first in average citation intensity per article. China and the United Kingdom were second and third, with 632 and 161 published articles, respectively. China and the Netherlands were second only to the United States regarding average citation counts.

3.2.2 Bibliometric analysis of the institution

A compilation of institutions with a publication count of ≥3 is presented in Table 3.

Table 3
Top 12 institutions contributing to publications

Num. Institution Location Publications

1 University of Toronto Canada 20

2 University of Pennsylvania America 14

3 University of California America 11

4 Brigham and women’s hospital America 8

5 University of Amsterdam Netherlands 8

6 Karolinska Institute Sweden 7

7 McMaster university Canada 7

8 Tehran university of medical sciences Iran 7

9 The university of Tokyo Japan 7

10 University of Queensland Australia 7

11 University of Washington America 7

12 University of Wisconsin Madison America 7

Num.	Institution	Location	Publications
1	University of Toronto	Canada	20
2	University of Pennsylvania	America	14
3	University of California	America	11
4	Brigham and women’s hospital	America	8
5	University of Amsterdam	Netherlands	8
6	Karolinska Institute	Sweden	7
7	McMaster university	Canada	7
8	Tehran university of medical sciences	Iran	7
9	The university of Tokyo	Japan	7
10	University of Queensland	Australia	7
11	University of Washington	America	7
12	University of Wisconsin Madison	America	7

The top 19 institutions regarding publication output were mainly located in the United States, Australia, Canada, the Netherlands, Iran, and Japan. The University of Toronto published twenty articles, ranking jointly first among all institutions. The above institutions are mainly universities but also include others, such as national medical centers.

3.2.3 Bibliometric analysis of the journal

After counting the number of articles published in journals, this study compiled a list of journals with a publication count of ≥10, along with their citation counts, and calculated the average number of citations per article, as presented in Table 4.

Table 4
Top 12 journals contributing to publications

Num. Journal Publications Citations Average

1 International journal of environmental research and public health 46 61 1.33

2 Journal of nursing management 32 729 22.78

3 Plos one 25 273 10.92

4 Journal of occupational and environmental medicine 18 121 6.72

5 Bmc medical education 16 62 3.88

6 Journal of clinical nursing 16 47 2.94

7 Bmc nursing 13 133 10.23

8 Bmj open 11 59 5.36

9 International journal of nursing studies 11 504 45.82

10 Journal of advanced nursing 11 464 42.18

11 Journal of surgical education 11 56 5.09

12 Frontiers in psychiatry 10 17 1.70

Num.	Journal	Publications	Citations	Average
1	International journal of environmental research and public health	46	61	1.33
2	Journal of nursing management	32	729	22.78
3	Plos one	25	273	10.92
4	Journal of occupational and environmental medicine	18	121	6.72
5	Bmc medical education	16	62	3.88
6	Journal of clinical nursing	16	47	2.94
7	Bmc nursing	13	133	10.23
8	Bmj open	11	59	5.36
9	International journal of nursing studies	11	504	45.82
10	Journal of advanced nursing	11	464	42.18
11	Journal of surgical education	11	56	5.09
12	Frontiers in psychiatry	10	17	1.70

The journal with the highest number of publications was “International Journal of Environmental Research and Public Health” with a total of 46 articles, followed by “Journal of Nursing Management” with 32 articles, which had the highest number of citations, reaching 729. Regarding the average citations per article, the top-ranked journal was “International Journal of Nursing Studies,” with an average of 45.82 citations per article, followed by “ Journal of Advanced Nursing,” with an average of 42.18 citations per article.

3.3 Analysis of co-citation

3.3.1 Analysis of co-citation journals

This study extracted the top 14 co-cited articles, as presented in Table 5.

Table 5
Top 14 contributing co-citation journals

Num. Co-citation Journals Counts Centrality

1 Journal of applied psychology 71 0.2

2 Journal of advanced nursing 65 0.08

3 International journal of nursing studies 61 0.14

4 Journal of occupational and environmental medicine 55 0.18

5 Journal of nursing management 47 0.07

6 Plos one 44 0.21

7 Jama-journal of the american medical association 43 0.12

8 International journal of environmental research and public health 42 0.04

9 Journal of clinical nursing 38 0.03

10 Bmj-british medical journal 36 0.11

11 Lancet 35 0.11

12 Bmj open 34 0.05

13 Occupational and environmental medicine 30 0.07

14 Bmc public health 30 0.05

Num.	Co-citation Journals	Counts	Centrality
1	Journal of applied psychology	71	0.2
2	Journal of advanced nursing	65	0.08
3	International journal of nursing studies	61	0.14
4	Journal of occupational and environmental medicine	55	0.18
5	Journal of nursing management	47	0.07
6	Plos one	44	0.21
7	Jama-journal of the american medical association	43	0.12
8	International journal of environmental research and public health	42	0.04
9	Journal of clinical nursing	38	0.03
10	Bmj-british medical journal	36	0.11
11	Lancet	35	0.11
12	Bmj open	34	0.05
13	Occupational and environmental medicine	30	0.07
14	Bmc public health	30	0.05

The journal with the highest number of total citations was “Journal of Applied Psychology,” with a total of 71 citations, followed by “Journal of Advanced Nursing,” “ International Journal of Nursing Studies,” and “Journal of Occupational and Environmental Medicine.” “PLOS One” and “Journal of Applied Psychology” had higher centrality, indicating that these two journals hold relatively important positions in this field.

3.3.2 Analysis of co-citation references

This study obtained information on co-citation references using CiteSpace, as shown in Fig. 4.

Fig.4

Top four references with the strongest citation bursts.

Lai and Spoorthy’s paper had a higher strength of emergence, with a strength of approximately 3 [30, 31]. Combined with the burst illustration in Fig. 4, these two articles have been commonly referenced since 2021 and are expected to maintain a high impact in the field in the near future.

3.4 Bibliometric analysis of the keywords

3.4.1 Keyword clustering

This study used CiteSpace to cluster the co-occurrence network of keywords and formed 11 clusters, as shown in Fig. 5.

Fig. 5

Co-citation network cluster view.

The clusters were numbered in descending order according to their range, from largest to smallest, as follows: #0 aged, #1 clinical competence, #2 COVID-19, #3 job satisfaction, #4 controlled study, #5 healthcare personnel, #6 questionnaire, #7 middle-aged, #8 employee performance appraisal, #9 workplace, and #10 nurses. Keyword directions were centered around age, training, and performance appraisal.

3.4.2 Keyword co-occurrence

This study used Vosviewer to generate a keyword co-occurrence network visualization of 8003 keywords from 1408 articles. First, this study analyzed the research hotspots and visualized keywords with a frequency of occurrence ≥51; the resulting density visualization is shown in Fig. 6

Fig. 6

Keyword density visualization.

By selecting and extracting factors influencing work performance from Fig. 6, we can observe that the nodes related to “burnout,” “anxiety,” “psychology,” and “job satisfaction” are notably highlighted in yellow compared to other nodes, indicating that these keywords have received significant attention. Among them, “psychology” was mentioned most frequently, with 221 mentions, followed by “job satisfaction” and “burnout” with 202 and 119 mentions, respectively. Additionally, research on topics such as “sleep,” “workplace,” and “workload” is also highly popular.

Next, this study analyzed the evolving trends in research hotspots after filtering the keywords and using Vosviewer to export the overlay visualization, as shown in Fig. 7.

Fig. 7

Keyword overlay visualization.

From the legend in the bottom-right corner of Fig. 7, it is evident that the color of nodes, moving from right to left, changes from purple to red, representing the newer years of the appearance of the keywords. Therefore, within the purple area of the figure (around 2015), the keywords primarily focus on “clinical competence,” “standard,” “decision making,” “sex difference,” and similar topics.

Continuing leftward in the figure, within the central yellow-green area (around 2017), the research emphasis shifts towards topics such as “sleep,” “workload,” “physician,” “health status,” and related aspects.

Finally, in the top-left corner of the figure, within the orange-red area (around 2019), the research focus predominantly centers on “burnout,” “mental health,” “anxiety,” “depression,” and similar subjects.

3.5 Comparison of influencing factors before and after the COVID-19 pandemic

Based on the results of the bibliometric analysis, this study found a clear shift in the focus on influencing factors over time. The emphasis has transitioned from early factors, such as clinical competence, standardization, and decision-making, to factors, including burnout, mental health, anxiety, and depression. We believe that the global COVID-19 pandemic that occurred during this period may be a significant reason for this shift in research focus. Therefore, this study identified 37 articles for analysis (The method for article extraction is illustrated in Fig. 1 of “2.2. Selection criteria”, which includes articles that focus on the work performance of the medical profession, explicitly present the specific content of influencing factors, and discuss their specific impact on work performance.) and organized them into a table (Table 6), with entries including author, year, country, population, methods, sample size, influencing factors, and COVID-19 occurrence.

Table 6
Extraction of influencing factors

Author, year Country Population Methods Sample Size Influencing Factors COVID-19 Pandemic

Popescu et al., 2022 [32] Romania Hospital’s employees Questionnaire 109 Fatigue; The culture of patient safety; Patient identification policy; Health information systems No

Akkoc et al., 2022 [33] Turkey Nurses Questionnaire 393 Ethical Climate; Innovative Culture No

Stimpfel et al., 2022 [34] America Nurses Focus groups 23 Over time; Shift Work; Understaffing; Overtime; Stress; Poor sleep No

Liu et al., 2022 [35] China Primary care providers Questionnaire 1287 Job Satisfaction; Social Support No

Montgomery et al., 2022 [36] America Nurses Survey 928 Burnout; Work Environment No

Wang et al., 2022 [37] China Primary care providers Questionnaire 1430 Financial Incentives No

Adynski et al., 2022 [38] America Nurses Semi-structured interviews 18 Work Environment; Support; Workload; Material Resources No

Hadi-Moghaddam et al., 2021 [39] Iran Nurses Questionnaire 431 Nurse-Supervisor Trust No

Sarikose et al., 2022 [18] Turkey Nurses Questionnaire 1000 Clinical Orientation Programmes; Training Sessions; Staffing; Manager Ability; Support; Educational Level; Colleague Solidarity No

Montgomery et al., 2021 [40] America Nurses Questionnaire 928 Burnout No

Liao et al., 2020 [41] China (Taiwan) Nurses Questionnaire 296 The New IT Charging System No

Dyrbye et al., 2019 [42] America Nurses Questionnaire 812 Sex; Burnout; Fatigue No

Maswadi et al., 2019 [43] Jordan Medical residents Questionnaire 555 Workload; Female Gender; Sleep deprivation; Colleagues; Income; Program No

Tong et al., 2018 [44] China Nurses Questionnaire 397 Education level; Work unit; Employment type No

Hu et al., 2018 [45] China (Taiwan) Nurses Questionnaire 335 Monthly salary; Social support No

Olsen et al., 2017 [46] Norway Nurses Questionnaire 2946 Job resources; S tress; Climate No

Yu et al., 2017 [47] South Korea Nurses Questionnaire 211 Self-leadership; Communication competency No

Hsieh et al., 2016 [48] China (Taiwan) Nurses Questionnaire 240 Friendly and humane work environment No

Chao et al., 2015 [17] China (Taiwan) Health care workers Questionnaire 344 Workplace stress; No

Lindegard et al., 2014 [49] Sweden Health care workers Questionnaire 770 Musculoskeletal pain; Perceived stress; No

Mrayyan et al., 2008 [50] Jordan Nurses Questionnaire 640 Job assignment; Marital status; Years of experience; No

Celik et al., 2007 [51] Turkey Nurses Questionnaire 622 Verbal and physical abuse No

Kagan et al., 2018 [15] Israel Nurses Questionnaire 87 Long hours in isolation rooms No

Kalmbach et al., 2017 [16] America Nondepressed interns Questionnaire 1215 Short sleep; Work hours; Depression status No

Karatepe et al., 2023 [52] Turkey Nurses Questionnaire 664 Clinical leadership; Creative team climate; Structural empowerment No

Jurado et al., 2022 [53] Spain Nurses Questionnaire 1013 Violence Yes

Sun et al., 2022 [54] China Health employees Questionnaire 669 Stress; Depression; Anxiety Yes

Ong et al., 2020 [55] Singapore Health care workers Questionnaire 243 PPE usage Yes

Munoz-Munoz et al., 2022 [56] Spain Nurses Questionnaire 456 Worry; Fatigue; Sleep problems; Fear; Anxiety; Stress Yes

Steinmetz et al., 2022 [57] Argentina Health care workers Questionnaire 305 Depression; Anxiety; Ancertainty about the COVID-19 infection Yes

Lou et al., 2022 [58] Canada Health care providers Questionnaire 119 Stress; Burnout; Adaptive coping strategies Yes

Sarfraz et al., 2022 [59] Pakistan Health care workers Questionnaire 490 Fear of being infected; Lack of protective measures Yes

Swaray et al., 2021 [60] Ghana Medical Laboratory Professionals Questionnaire 473 Depression; Anxiety; Stress Yes

Yadeta et al., 2021 [61] Ethiopia Health care workers Questionnaire 265 Depression; Anxiety; Uncertainty about the COVID-19 infection Yes

Kim-Godwin et al., 2021 [62] America Nurses Questionnaire 215 Stress; Sleep quality Yes

Pourteimour et al., 2021 [63] Iran Nurses Questionnaire 161 Mental load; Age; Gender; Type of ward; Working shift; Experience of providing care to patients Yes

Labrague et al., 2020 [64] Oman Nurses Questionnaire 325 Organizational support; Personal resilience; Social support Yes

Author, year	Country	Population	Methods	Sample Size	Influencing Factors	COVID-19 Pandemic
Popescu et al., 2022 [32]	Romania	Hospital’s employees	Questionnaire	109	Fatigue; The culture of patient safety; Patient identification policy; Health information systems	No
Akkoc et al., 2022 [33]	Turkey	Nurses	Questionnaire	393	Ethical Climate; Innovative Culture	No
Stimpfel et al., 2022 [34]	America	Nurses	Focus groups	23	Over time; Shift Work; Understaffing; Overtime; Stress; Poor sleep	No
Liu et al., 2022 [35]	China	Primary care providers	Questionnaire	1287	Job Satisfaction; Social Support	No
Montgomery et al., 2022 [36]	America	Nurses	Survey	928	Burnout; Work Environment	No
Wang et al., 2022 [37]	China	Primary care providers	Questionnaire	1430	Financial Incentives	No
Adynski et al., 2022 [38]	America	Nurses	Semi-structured interviews	18	Work Environment; Support; Workload; Material Resources	No
Hadi-Moghaddam et al., 2021 [39]	Iran	Nurses	Questionnaire	431	Nurse-Supervisor Trust	No
Sarikose et al., 2022 [18]	Turkey	Nurses	Questionnaire	1000	Clinical Orientation Programmes; Training Sessions; Staffing; Manager Ability; Support; Educational Level; Colleague Solidarity	No
Montgomery et al., 2021 [40]	America	Nurses	Questionnaire	928	Burnout	No
Liao et al., 2020 [41]	China (Taiwan)	Nurses	Questionnaire	296	The New IT Charging System	No
Dyrbye et al., 2019 [42]	America	Nurses	Questionnaire	812	Sex; Burnout; Fatigue	No
Maswadi et al., 2019 [43]	Jordan	Medical residents	Questionnaire	555	Workload; Female Gender; Sleep deprivation; Colleagues; Income; Program	No
Tong et al., 2018 [44]	China	Nurses	Questionnaire	397	Education level; Work unit; Employment type	No
Hu et al., 2018 [45]	China (Taiwan)	Nurses	Questionnaire	335	Monthly salary; Social support	No
Olsen et al., 2017 [46]	Norway	Nurses	Questionnaire	2946	Job resources; S tress; Climate	No
Yu et al., 2017 [47]	South Korea	Nurses	Questionnaire	211	Self-leadership; Communication competency	No
Hsieh et al., 2016 [48]	China (Taiwan)	Nurses	Questionnaire	240	Friendly and humane work environment	No
Chao et al., 2015 [17]	China (Taiwan)	Health care workers	Questionnaire	344	Workplace stress;	No
Lindegard et al., 2014 [49]	Sweden	Health care workers	Questionnaire	770	Musculoskeletal pain; Perceived stress;	No
Mrayyan et al., 2008 [50]	Jordan	Nurses	Questionnaire	640	Job assignment; Marital status; Years of experience;	No
Celik et al., 2007 [51]	Turkey	Nurses	Questionnaire	622	Verbal and physical abuse	No
Kagan et al., 2018 [15]	Israel	Nurses	Questionnaire	87	Long hours in isolation rooms	No
Kalmbach et al., 2017 [16]	America	Nondepressed interns	Questionnaire	1215	Short sleep; Work hours; Depression status	No
Karatepe et al., 2023 [52]	Turkey	Nurses	Questionnaire	664	Clinical leadership; Creative team climate; Structural empowerment	No
Jurado et al., 2022 [53]	Spain	Nurses	Questionnaire	1013	Violence	Yes
Sun et al., 2022 [54]	China	Health employees	Questionnaire	669	Stress; Depression; Anxiety	Yes
Ong et al., 2020 [55]	Singapore	Health care workers	Questionnaire	243	PPE usage	Yes
Munoz-Munoz et al., 2022 [56]	Spain	Nurses	Questionnaire	456	Worry; Fatigue; Sleep problems; Fear; Anxiety; Stress	Yes
Steinmetz et al., 2022 [57]	Argentina	Health care workers	Questionnaire	305	Depression; Anxiety; Ancertainty about the COVID-19 infection	Yes
Lou et al., 2022 [58]	Canada	Health care providers	Questionnaire	119	Stress; Burnout; Adaptive coping strategies	Yes
Sarfraz et al., 2022 [59]	Pakistan	Health care workers	Questionnaire	490	Fear of being infected; Lack of protective measures	Yes
Swaray et al., 2021 [60]	Ghana	Medical Laboratory Professionals	Questionnaire	473	Depression; Anxiety; Stress	Yes
Yadeta et al., 2021 [61]	Ethiopia	Health care workers	Questionnaire	265	Depression; Anxiety; Uncertainty about the COVID-19 infection	Yes
Kim-Godwin et al., 2021 [62]	America	Nurses	Questionnaire	215	Stress; Sleep quality	Yes
Pourteimour et al., 2021 [63]	Iran	Nurses	Questionnaire	161	Mental load; Age; Gender; Type of ward; Working shift; Experience of providing care to patients	Yes
Labrague et al., 2020 [64]	Oman	Nurses	Questionnaire	325	Organizational support; Personal resilience; Social support	Yes

As shown in Table 6, 25 of the 37 articles were based on non-COVID-19 situations, while 12 focused on the COVID-19 pandemic in their research background. The factors that influence work performance mentioned in Table 6 are organized from macro- and micro-perspectives, definitions, and relevant reference literature, as shown in Table 7.

Table 7

Explanation for influencing factors

Num.	Influencing Factors	Explanation	Related Documents	Micro/Macro
1	Burnout	Healthcare workers may experience mental exhaustion, dissociation from their duties, and negative feelings towards their job due to long-term work pressure	Montgomery et al. (2022); Dyrbye et al. (2019). Montgomery et al. (2021); Lou et al. (2022)	Micro
2	Fatigue	Healthcare workers experience physical exhaustion or weariness as a result of prolonged working hours or other related conditions	Popescu et al. (2022); Dyrbye et al. (2021). Steinmetz et al. (2022); Munoz-Munoz et al. (2022)	Micro
3	Stress	Various pressures stemming from societal, occupational, and personal factors	Stimpfel et al. (2022); Olsen et al. (2017). Chao et al. (2015); Lindegard et al. (2014). Sun et al. (2018); Munoz-Munoz et al. (2022). Swaray et al. (2021);Lou et al. (2022); Kim-Godwin et al. (2021);Pourteimour et al. (2021)	Micro
4	Sleep	Sleep-related issues among healthcare workers, including insomnia, insufficient sleep, deteriorated sleep quality, and sleep disorders	Stimpfel et al. (2022); Kalmbach et al. (2017). Maswadi et al. (2019); Munoz-Munoz et al. (2022). Kim-Godwin et al. (2021)	Micro
5	Depression	A psychological disorder characterized by persistent sadness, feelings of hopelessness, and loss of interest in previously enjoyable activities	Sun et al. (2022); Kalmbach et al. (2017). Steinmetz et al. (2022); Yadeta et al. (2021). Swaray et al. (2021)	Micro
6	Anxiety	A psychological state characterized by worry, tension, and unease about future events or uncertain outcomes	Sun et al. (2022); Munoz-Munoz et al. (2022). Steinmetz et al. (2022); Yadeta et al. (2021). Swaray et al. (2021)	Micro
7	Worry	Excessive and persistent rumination of healthcare workers on future potential events or outcomes	Munoz-Munoz et al. (2022)	Micro
8	Fear	Unpleasant emotional response caused by perceiving threat, danger, or harm	Munoz-Munoz et al. (2022); Steinmetz et al. (2022). Sarfraz et al. (2022)	Micro
9	Personal competence	Personal characteristics of healthcare workers, such as their age, sex, educational level, and work experience	Sarikose et al. (2022); Dyrbye et al. (2019). Tong et al. (2018); Yu et al. (2017). Mrayyan et al. (2008); Karatepe et al. (2023). Maswadi et al. (2019); Pourteimour et al. (2021)	Micro
10	Physical health	The overall physical condition of the healthcare workers, including muscle and bone pain conditions, whether they can normal activities and other aspects.	Lindegard et al.(2014);Ong et al.(2022)	Macro
11	Violence	In healthcare institutions, healthcare workers may experience verbal or physical assaults, abuse, or threats from patients, patients’ families, visitors, doctors, or other healthcare staff	Celik et al. (2007); Jurado et al. (2022)	Macro
12	Climate	The work environment and organizational culture in which healthcare workers operate	Popescu et al. (2022);Akkoç et al. (2022). Montgomery et al. (2022); Adynski et al. (2022). Olsen et al. (2017); Hsieh et al. (2016). Karatepe et al. (2023)	Macro
13	Perceived interdependence	Healthcare workers interdependence among themselves and others can manifest in different ways, including social support, organizational support, trust, and other forms of mutual reliance	Liu et al. (2022); Adynski et al. (2022). Hadi-Moghaddam et al. (2021); Sarikose et al. (2022). Maswadi et al. (2019); Hu et al. (2018). Kagan et al. (2018); Labrague et al. (2020)	Macro
14	Manager ability	The abilities and levels of healthcare workers to participate in leadership, organizational work environment, and management	Sarikose et al. (2022);	Macro
15	Strategy	The plans and approaches adopted by healthcare organizations to achieve their objectives	Popescu et al. (2022); Maswadi et al. (2019). Pourteimour et al. (2021); Lou et al. (2022)	Macro
16	Staffing	The activities of scheduling, arranging, and managing personnel in various departments within healthcare institutions	Stimpfel et al. (2022); Sarikose et al. (2023). Adynski et al. (2022); Maswadi et al. (2019). Tong et al. (2018);Mrayyan et al.(2008); Kalmbach et al. (2017)	Macro
17	Financial incentives	To encourage or reward healthcare workers or teams, monetary or other welfare measures are provided	Wang et al. (2022);Maswadi et al.(2019); Hu et al. (2018)	Macro
18	Training sessions	To enhance the skills and knowledge of healthcare workers, training programs are implemented	Sarikose et al. (2022)	Macro
19	Resource	In healthcare institutions, the provision of essential materials and technological support	Sarfraz et al. (2021); Popescu et al. (2022). Liao et al. (2020); Olsen et al. (2017). Adynski et al. (2022)	Macro

Table 7 presents 19 factors that influence work performance, including 9 macro and 10 micro factors. This study plotted a comparative graph based on the frequency of occurrence (the proportion of articles that mentioned a particular factor to the total number of articles in that context) of the 19 influencing factors to gain a deeper understanding of the attention paid to these factors during both non-related and related COVID-19 pandemic periods.

In Fig. 8, the blue and orange bars represent articles unrelated and related to the COVID-19 pandemic, respectively. The following research findings were obtained through analysis:

Fig. 8

Percentage of frequency of occurrence.

At the micro level, regarding the influencing factors on work performance during the non-pandemic period, the most significant factor cited was personal competence, accounting for approximately 28.0%, followed by stress at approximately 16.0%. Sleep and burnout accounted for approximately 12.0%, while anxiety, worry, and fear were not considered during the non-pandemic period. Conversely, for factors influencing work performance during the COVID-19 pandemic, the most prominent factor was stress, accounting for approximately 50.0%, while anxiety accounted for approximately 41.7%. Fear and depression accounted for approximately 33.3%, while sleep and fatigue accounted for approximately 16.7%.

At the macro level, for influencing factors on work performance during the non-pandemic period, the significant factors were staffing, perceived interdependence, and climate, accounting for approximately 28.0%, followed by resources at approximately 16.0%. Financial incentives were also a significant factor, accounting for approximately 12.0%. For influencing factors on work performance during the COVID-19 pandemic, the most significant factor was strategy, accounting for approximately 16.7%, followed by perceived interdependence, violence, and resources, accounting for approximately 8.3%. However, the other five macro-level factors were not considered.

4 Discussion

4.1 Research trends on influencing factors of healthcare workers’ work performance

In this study, we conducted a bibliometric analysis of 1408 articles related to the factors influencing healthcare workers’ work performance. We examined various aspects, including the annual trend of article publications, descriptive analysis (including country, institution, and journal analysis), co-cited references analysis (including co-cited journal and reference analysis), and keyword analysis (including keyword clustering and co-occurrence analysis).

First, the analysis of the annual trend of article publications (Fig. 2) revealed a significant increase in the number of publications after 2020, indicating an unprecedented surge in research related to the factors influencing healthcare workers’ work performance due to the COVID-19 pandemic. During the COVID-19 pandemic, the factors affecting work performance exhibited unique characteristics. For instance, Sarfraz et al. emphasized healthcare workers’ fears of contracting the virus and anxiety related to the shortage of medical resources due to their exposure to highly contagious and urgent work environments [18]. Ong et al. also highlighted physical issues, such as headaches caused by wearing personal protective equipment, which impacted work performance [55]. Furthermore, scholars, including Lulli and Yadeta, emphasized the necessity of studying work performance factors in healthcare during the COVID-19 pandemic [61, 65]. Comparing the periods before and during the COVID-19 pandemic is essential to understand and clarify the changing trends.

Second, in the analysis of the Strongest Citation Bursts (Fig. 4), two articles published during the early stages of the COVID-19 pandemic, Lai (2020) and Spoorthy (2020), exhibited significant citation bursts [30, 31]. Lai (2020) revealed that healthcare workers were more susceptible to symptoms, such as depression, anxiety, and insomnia, due to the impact of the pandemic, necessitating immediate special interventions, particularly for women, nurses, and frontline healthcare workers [31]. Spoorthy (2020) also discussed the psychological health challenges experienced by healthcare workers during the pandemic and suggested psychological therapeutic approaches based on stress adaptation models to address these issues [30]. These studies indicated that the most significant impact of the COVID-19 pandemic on healthcare workers was not only an increase in patient numbers and workload but also psychological health issues caused by the pandemic’s uncertainty and high contagiousness. This aspect was further confirmed in subsequent related research [66].

Third, in the keyword overlay visualization (Fig. 7), this study observed changes in the focus of work performance factors over time, with the left side representing the more recent period. This indirectly indicates a shift in research priorities. Before the COVID-19 pandemic, the main focus of work performance factors was on clinical competence, standards, and decision-making. However, with the onset of the COVID-19 pandemic, the emphasis shifted toward issues related to burnout, mental health, anxiety, depression, and other psychological health problems among healthcare workers.

4.2 Comparison of influencing factors of work performance before and after the COVID-19 pandemic from macro and micro perspectives

Through a supplementary comparative analysis of the 37 articles, it can be observed that:

At the micro level, the highest proportion during the non-pandemic period was personal competence, indicating that personal abilities and work experience significantly impacted work performance during this period. During the COVID-19 pandemic, stress had the highest proportion, indicating that the greatest change of the pandemic in the healthcare setting was the increase in stress experienced by healthcare workers, which affected their work performance. There was also increased attention to fear, worry, and anxiety, which were not focused on during the non-pandemic period. This suggests that during the COVID-19 pandemic, particularly in the early stages, healthcare workers’ psychological states were affected by factors, including high mortality rates, limited understanding of the infectious agent, and lack of specific treatment plans.

At the macro level, the highest proportions during the pre-COVID-19 pandemic were staffing, climate, and perceived interdependence, while during the COVID-19 pandemic, strategy had the highest proportion, possibly attributed to the sudden changes in the working environment in healthcare settings caused by the COVID-19 pandemic. Therefore, healthcare institutions should develop emergency response plans from a strategic perspective to ensure that healthcare workers receive and treat more patients while ensuring their safety. During the COVID-19 pandemic, healthcare institutions reduced their focus on training sessions, financial incentives, staffing, manager ability, and climate and only focused on strategy, perceived interdependence, violence, and resources. This indicates that in emergencies, such as a pandemic, it is important to provide sufficient medical resources to enhance healthcare workers’ sense of security and create a harmonious working atmosphere, thereby reducing their psychological stress and improving work performance [33, 38].

Furthermore, by comparing the macro- and micro-level factors, this study observed no significant difference in the emphasis on macro- and micro-level factors during the non-pandemic period. However, during the COVID-19 pandemic, healthcare institutions mainly focused on micro-level factors. This indirectly indicates that the COVID-19 pandemic not only increased the workload of healthcare workers but also imposed a greater psychological burden owing to the uncertainty surrounding the pandemic.

4.3 Practical value of the study

Multiple factors frequently influence the work performance of healthcare workers. By conducting a comparative analysis of influencing factors before and after the COVID-19 pandemic, this study can provide the following guidance for preventing a decline in work performance during future urgent events similar to the COVID-19 outbreak: First, during special periods, healthcare managers should recognize the psychological well-being of their staff. The focus should be on micro-level factors closely associated with this period, such as fear and worry. Positive measures that address these concerns should be implemented to mitigate the factors affecting frontline healthcare workers’ performance, such as conducting psychological counseling sessions and developing corresponding measures to prevent the occurrence of medical violence conflicts. Second, healthcare institutions should develop timely plans from a macro-level strategic perspective. This includes rational workload planning tailored to specific circumstances, ensuring an adequate supply of medical resources, and providing timely medical training. These actions will help healthcare workers adapt to new situations and safely provide timely care and treatment to patients. In summary, to improve the work performance of healthcare workers, medical institutions can optimize and improve in various aspects. By focusing on the mental health of employees, developing macro-strategic plans, improving the work environment, and enhancing team collaboration capabilities, medical institutions can better respond to future emergencies similar to the COVID-19 pandemic and ensure that healthcare workers can provide high-quality medical services.

4.4 Limitations

This study had some limitations. First, this study qualitatively identified the influencing factors on work performance without quantitatively measuring the impact of each factor on work performance. Second, this study was limited to identifying and comparing the influencing factors on work performance without providing specific improvement strategies to enhance work performance. Furthermore, this study was solely based on a literature research and lacked empirical research. Therefore, field investigations and studies in healthcare institutions are needed to validate our findings. Finally, in this study, when extracting the factors influencing healthcare workers’ work performance, this study only searched the SCIE and SSCI indexes of the Web of Science core database. Therefore, the selected 37 articles may have limitations in terms of extracting influencing factors.

5 Conclusion

This study used bibliometric methods to review 1408 articles on work performance in the healthcare field between 2010 and 2023. CiteSpace and Vosviewer were used to provide an overview of the countries, institutions, journals, co-cited journals, references, and keywords of the articles to provide insights into the development trends and frontiers of research on work performance in the healthcare field. And 37 articles related to factors influencing work performance were selected from 1408 articles for supplementary analysis, ten micro- and nine macro-level influencing factors related to healthcare work performance were extracted. Based on this, a comparative analysis of the frequency of occurrence of the aforementioned factors influencing work performance before and after the COVID-19 pandemic was conducted.

The results addressed limitations in previous studies, such as incomplete extraction of factors influencing work performance and unclear comparisons of changes before and after the COVID-19 pandemic. This study not only supplements the shortcomings of research on healthcare work performance at the theoretical level but also provides insights and guidance for improving the work performance of healthcare workers.

Ethical approval

This study was reviewed and approved by the Biological and Medical Ethics Committee, Northeastern University, China (ID: NEU-EC-2021B003S).

Informed consent

Not applicable.

Conflict of interest

The authors declare that they have no conflict of interest to disclose.

Footnotes

Acknowledgments

This research has received valuable views and suggestions from experts in the medical care field. We would like to express our sincere gratitude to all experts and scholars for their help in completing this thesis. We would also like to thank the editor and anonymous reviewers for their valuable comments and advice.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 72171042) and the Fundamental Research Funds for the Central Universities (Grant No. N2406006).

References

Jin

, Yao

, Xiao

, Qu

, Fu

. Effects of nursing workload on medication administration errors: A quantitative study. Work-a Journal of Prevention Assessment & Rehabilitation. 2023;74(1):247–54.

Vatani

, Arami

, Khanikosarkhizi

, Rabori

MAS

, Khandan

, Dehghan

, et al. Safety climate and related factors in rehabilitation nurses of hospitals in Iran. Work-a Journal of Prevention Assessment & Rehabilitation. 2021;68(1):189–96.

Elliott

, Camacho

, Jankovic

, Sculpher

, Faria

. Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Quality & Safety. 2021;30(2):96–105.

Ebrahimi

, Jafarjalal

, Lotfolahzadeh

, Moghadam

SMK

. The effect of workload on nurses’ quality of life with moderating perceived social support during the COVID-19 pandemic. Work-a Journal of Prevention Assessment & Rehabilitation. 2021;70(2):347–54.

Kohn

, Corrigan

, Donaldson

. Institute of Medicine. To err is human: building a safer health system. Washington, DC: National Academy Press; 2000.

Jin

, Qu

, Munechika

, Sano

, Kajihara

, Duffy

, et al. Applying intelligent algorithms to automate the identification of error factors. Journal of Patient Safety. 2021;17(8):E918–28.

Pullam

, Russell

, White-Lewis

. Frequency of medication administration timing error in hospitals: A systematic review. Journal of Nursing Care Quality. 2023;38(2):126–33.

Huang

CLC

, Wu

, Ho

, Wang

. Risks of treated anxiety, depression, and insomnia among nurses: A nationwide longitudinal cohort study. PLoS One. 2018;13(9).

Kaewboonchoo

, Yingyuad

, Rawiworrakul

, Jinayon

. Job stress and intent to stay at work among registered female nurses working in Thai hospitals. Journal of Occupational Health. 2014;56(2):93–9.

10.

Lan

, Zhao

, Yang

. Study for the effects of nurse extended working hours on daily fatigue and job satisfaction. Journal of Nurses Training. 2007;24(1):2214–6.

11.

Lee

, Ko

. Effects of self-efficacy, affectivity and collective efficacy on nursing performance of hospital nurses. Journal of Advanced Nursing. 2010;66(4):839–48.

12.

Demir

, Sahin

, Atalay

, Aslan

, Elboga

, Altindag

. How did the psychological impact of COVID-19 change healthcare workers in times of fighting the pandemic?. Work-a Journal of Prevention Assessment & Rehabilitation. 2022;72(1):19–26.

13.

Organisation

. Statement on the fifteenth meeting of the IHR Emergency Committee on the COVID-19 pandemic 2023 [updated 2023 May 5; cited 2023 May 30]. Available from: https://www.who.int.

14.

Al-Makhaita

, Sabra

, Hafez

. Job performance among nurses working in two different health care levels, Eastern Saudi Arabia: a comparative study. International Journal of Medical Science and Public Health. 2014;3(7):832–7.

15.

Kagan

, Fridman

, Shalom

, Melnikov

. The effect of working in an infection isolation room on hospital nurses’ job satisfaction. Journal of Nursing Management. 2018;26(2):120–6.

16.

Kalmbach

, Arnedt

, Song

, Guille

, Sen

. Sleep disturbance and short sleep as risk factors for depression and perceived medical errors in first-year residents. Sleep. 2017;40(3):zsw073.

17.

Chao

, Jou

, Liao

, Kuo

. Workplace stress, job satisfaction, job performance, and turnover intention of health ware workers in rural Taiwan. Asia-Pacific Journal of Public Health. 2015;27(2):NP1827–36.

18.

Sarikose

, Goktepe

. Effects of nurses’ individual, professional and work environment characteristics on job performance. Journal of Clinical Nursing. 2022;31(5-6):633–41.

19.

Diem

, Wolter

. The use of bibliometrics to measure research performance in education sciences. Research in Higher Education. 2013;54(1):86–114.

20.

Chen

, Chen

, Zhang

, Hao

. Discovering the recent research in natural language processing field based on a statistical approach. SETE@ICWL. 2017;undefined:507–17.

21.

Chen

, Weng

, Hao

. A data-driven approach for discovering the recent research status of diabetes in China. International Conference on Health Information Science. 2017; undefined:undefined.

22.

, Zhao

. Bibliometric analysis of global environmental assessment research in a 20-year period. Environmental Impact Assessment Review. 2015;50:158–66.

23.

Mayr

, Scharnhorst

. Scientometrics and information retrieval: weak-links revitalized. Scientometrics. 2015;102(3):2193–9.

24.

Abramo

, D’Angelo

, Viel

. The field-standardized average impact of national research systems compared to world average: the case of Italy. Scientometrics. 2011;88(2):599–615.

25.

Virani

, Wellstead

, Howlett

. Where is the policy? A bibliometric analysis of the state of policy research on medical tourism. Global Health Research and Policy. 2020;5:19.

26.

Liao

, Tang

, Luo

, Li

, Chiclana

, Zeng

X-J

. A bibliometric analysis and visualization of medical big data research. Sustainability. 2018;10(1):166.

27.

van Eck

, Waltman

. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523–38.

28.

Chen

. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology. 2006;57(3):359–77.

29.

Synnestvedt

, Chen

, Holmes

, editors. CiteSpace II: visualization and knowledge discovery in bibliographic databases. AMIA annual symposium proceedings; 2005: American Medical Informatics Association.

30.

Spoorthy

, Pratapa

, Mahant

. Mental health problems faced by healthcare workers due to the COVID-19 pandemic-a review. Asian Journal of Psychiatry. 2020;51:102119.

31.

Lai

, Ma

, Wang

, Cai

, Hu

, Wei

, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Network Open. 2020;3(3):e203976.

32.

Popescu

, El-Chaarani

, El-Abiad

, Gigauri

. Implementation of Health Information Systems to Improve Patient Identification. International Journal of Environmental Research and Public Health. 2022;19(22):15236.

33.

Akkoc

, Ture

, Arun

, Okun

. Mediator effects of psychological empowerment between ethical climate and innovative culture on performance and innovation in nurses. Journal of Nursing Management. 2022;30(7):2324–43.

34.

Stimpfel

, Goldsamt

, Liang

, Costa

. Work organization factors associated with nurses’ stress, sleep, and performance: a pre-pandemic analysis. Journal of Nursing Regulation. 2022;13(3):4–12.

35.

Liu

, Liu

, Gong

, Liang

. Relating medical errors to medical specialties: a mixed analysis based on litigation documents and qualitative data. Risk Management and Healthcare Policy. 2020;13:335–45.

36.

Montgomery

, Patrician

, Azuero

. Nurse burnout syndrome and work environment impact patient safety grade. Journal of Nursing Care Quality. 2022;37(1):87–93.

37.

Wang

, Zhao

, Liu

, Wang

, Yuan

. The association between financial incentives and job performance among primary care providers in six provinces of China. Risk Management and Healthcare Policy. 2022;15:2323–34.

38.

Adynski

, Leeman

, Adynski

, Ledford

, McQuide

, Bock

, et al. Nurses’ attitudes towards their jobs in outpatient human immunodeficiency virus facilities in Namibia: a qualitative descriptive study. Journal of Nursing Management. 2022;30(2):491–500.

39.

Hadi-Moghaddam

, Karimollahi

, Aghamohammadi

. Nurses’ trust in managers and its relationship with nurses’ performance behaviors: a descriptive-correlational study. BMC Nursing. 2021;20(1):132.

40.

Montgomery

, Azuero

, Baernholdt

, Loan

, Miltner

, Qu

, et al. Nurse burnout predicts self-reported medication administration errors in acute care hospitals. Journal for Healthcare Quality. 2021;43(1):13–23.

41.

Liao

, Lin

. Performance evaluation of an information technology intervention regarding charging for inpatient medical materials at a regional teaching hospital in Taiwan: Empirical Study. Jmir Mhealth and Uhealth. 2020;8(3).

42.

Dyrbye

, Shanafelt

, Johnson

, Satele

, West

. A cross-sectional study exploring the relationship between burnout, absenteeism, and job performance among American nurses. BMC Nursing. 2019;18(1):57.

43.

Maswadi

, Khader

, Abu

Slaih A

. Perceived stress among resident doctors in Jordanian Teaching Hospitals: cross-sectional study. Jmir Public Health and Surveillance. 2019;5(4):215–27.

44.

Tong

. Relationship between meaningful work and job performance in nurses. International Journal of Nursing Practice. 2018;24(2):e12620.

45.

, Yu

, Chang

, Lin

. Social support and factors associated with self-efficacy among acute-care nurse practitioners. Journal of Clinical Nursing. 2018;27(3-4):876–82.

46.

Olsen

, Bjaalid

, Mikkelsen

. Work climate and the mediating role of workplace bullying related to job performance, job satisfaction, and work ability: a study among hospital nurses. Journal of Advanced Nursing. 2017;73(11):2709–19.

47.

, Ko

. Communication competency as a mediator in the self-leadership to job performance relationship. Collegian. 2017;24(5):421–5.

48.

Hsieh

, Chiu

, Hsieh

, Ho

, Chen

, Chang

. The perceptions and expectations toward the social responsibility of hospitals and organizational commitment of nursing staff. Journal of Nursing Research. 2016;24(3):249–61.

49.

Lindegard

, Larsman

, Hadzibajramovic

, Ahlborg

. The influence of perceived stress and musculoskeletal pain on work performance and work ability in Swedish health care workers. International Archives of Occupational and Environmental Health. 2014;87(4):373–9.

50.

Mrayyan

, Al-Faouri

. Predictors of career commitment and job performance of Jordanian nurses. Journal of Nursing Management. 2008;16(3):246–56.

51.

Celik

, Celik

, Agirbas

, Ugurluoglu

. Verbal and physical abuse against nurses in Turkey. International Nursing Review. 2007;54(4):359–66.

52.

Karatepe

, Turkmen

. Nurse performance: a path model of clinical leadership, creative team climate and structural empowerment. Journal of Clinical Nursing. 2023;32(3-4):584–96.

53.

Jurado

MDM

, Linares

JJG

, Perez-Fuentes

, Martinez

. Aggression and burnout in nurses during COVID-19: a cross-sectional study. Nursing & Health Sciences. 2023;25(1):130–40.

54.

Sun

, Sarfraz

, Ivascu

, Iqbal

, Mansoor

. How did work-related depression, anxiety, and stress hamper healthcare employee performance during COVID-19? The mediating role of job burnout and mental health. International Journal of Environmental Research and Public Health. 2022;19(16):10359.

55.

Ong

JJY

, Bharatendu

, Goh

, Tang

JZY

, Sooi

KWX

, Tan

, et al. Headaches associated with personal protective equipment – a cross-sectional study among frontline healthcare workers during COVID-19. Headache. 2020;60(5):864–77.

56.

Munoz-Munoz

, Carretero-Bravo

, Perez-Munoz

, Diaz-Rodriguez

. Analysis of the psychosocial impact of the COVID-19 pandemic on the nursing staff of the intensive care units (ICU) in Spain. Healthcare. 2022;10(5):796.

57.

Steinmetz

LCL

, Herrera

, Fong

, Godoy

. A longitudinal study on the changes in mental health of healthcare workers during the COVID-19 pandemic. Psychiatry-Interpersonal and Biological Processes. 2022;85(1):56–71.

58.

Lou

, Montreuil

, Feldman

, Fried

, Lavoie-Tremblay

, Bhanji

, et al. Nurses’ and physicians’ distress, burnout, and coping strategies during COVID-19: Stress and impact on perceived performance and intentions to quit. Journal of Continuing Education in the Health Professions. 2022;42(1):E44–52.

59.

Sarfraz

, Hafeez

, Abdullah

, Ivascu

, Ozturk

. The effects of the COVID-19 pandemic on healthcare workers’ psychological and mental health: The moderating role of felt obligation. Work-a Journal of Prevention Assessment & Rehabilitation. 2022;71(3):539–50.

60.

Swaray

, Tetteh

, Ekem-Ferguson

, Awinibuno

IAN

, Adu-Gyasi

, Acheampong

, et al. Psychological distress amongst medical laboratory professionals involved in COVID-19-related duties: a nationally stratified cross-sectional survey, Ghana. Inquiry-the Journal of Health Care Organization Provision and Financing. 2021;58:469580211067479.

61.

Yadeta

, Dessie

, Balis

. Magnitude and predictors of health care workers depression during the COVID-19 pandemic: health facility-based study in Eastern Ethiopia. Frontiers in Psychiatry. 2021;12:654430.

62.

Kim-Godwin

, Lee

, Logan

, Liu

. Factors influencing sleep quality among female staff nurses during the early COVID-19 pandemic in the United States. International Journal of Environmental Research and Public Health. 2021;18(9):4827.

63.

Pourteimour

, Yaghmaei

, Babamohamadi

. The relationship between mental workload and job performance among Iranian nurses providing care to COVID-19 patients: a cross-sectional study. Journal of Nursing Management. 2021;29(6):1723–32.

64.

Labrague

, De los Santos

JAA

. COVID-19 anxiety among front-line nurses: Predictive role of organisational support, personal resilience and social support. Journal of Nursing Management. 2020;28(7):1653–61.

65.

Lulli

, Giorgi

, Pandolfi

, Foti

, Finstad

, Arcangeli

, et al. Identifying psychosocial risks and protective measures for workers’ mental wellbeing at the time of COVID-19: a narrative review. Sustainability. 2021;13(24):13869.

66.

Marvaldi

, Mallet

, Dubertret

, Moro

, Guessoum

. Anxiety, depression, trauma-related, and sleep disorders among healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews. 2021;126:252–64.