The Journal of Continuing Education in Nursing

Original Article 

Exploration of Factors Influencing Nurse Competence Through Nursing Profile Analysis

Weihua Jing, RN; Xianying Zhang, RN; Rongxiang Chi, RN; Xiufang Sun, RN; Shuguang Lv, RN

Abstract

Background:

Nurse competence is a combination of knowledge, performance, skills, and attitudes that are required in fulfilling one's role as a nurse. So far, few comprehensive studies have explored the influencing factors of nurse competence.

Method:

The competence levels of 160 RNs in a Chinese hospital were evaluated using a questionnaire method, and the relationship between competence results and nursing characteristics was analyzed.

Results:

The competence of participating nurses was at a medium level. Among all the characteristics, education and staffing levels were two main factors influencing nurse competence.

Conclusion:

Quantity of nurses and quality of nursing service are two main issues facing the modern nursing system. The findings from this study provide useful information and suggestions on how to improve nurse competence to nurse industry personnel, including nurses, nursing employers, educators, and students. [J Contin Educ Nurs. 2019;50(12):572–580.]

Abstract

Background:

Nurse competence is a combination of knowledge, performance, skills, and attitudes that are required in fulfilling one's role as a nurse. So far, few comprehensive studies have explored the influencing factors of nurse competence.

Method:

The competence levels of 160 RNs in a Chinese hospital were evaluated using a questionnaire method, and the relationship between competence results and nursing characteristics was analyzed.

Results:

The competence of participating nurses was at a medium level. Among all the characteristics, education and staffing levels were two main factors influencing nurse competence.

Conclusion:

Quantity of nurses and quality of nursing service are two main issues facing the modern nursing system. The findings from this study provide useful information and suggestions on how to improve nurse competence to nurse industry personnel, including nurses, nursing employers, educators, and students. [J Contin Educ Nurs. 2019;50(12):572–580.]

Competence and competency are two terms that are often confused, and the usage of these two terms are inconsistent in many publications. McMullan et al. (2003) suggested that competence is a description of action, behavior, and outcome of job-related performance, whereas competency refers to a person's characteristics and qualities that led to such performance. On the contrary, McConnell (2001) and Nolan (1998) stated that competence focuses on the capacity and ability of an individual to perform the job responsibilities, whereas competency focuses on the actual performance of an individual in performing a specific task. In this study, the latter definition is used, where competence refers to the capacity and ability and competency refers to the actual performance.

Regarding the definitions and attributes of nurse competence, to date there hasn't been a unified concept. Short (1984) raised that knowledge and skill are two main components defining competence. Milligan (1998) suggested that nurse education and training are two key fields in competence development. Bechtel, Davidhizar, and Bradshaw (1999) argued that critical thinking skills and mechanical skills are indispensable parts of competence. O'Shea (2002) defined nursing competence as the knowledge, skills, ability, and behaviors required in correctly performing nursing tasks. Fukada (2018) further extended nurse competence into three theories including behaviorism, trait theory, and holism: behaviorism treats competence as the ability to perform core skills and is evaluated by skill performance; trait theory treats competence as the individual traits such as knowledge and critical thinking skills required for performing nursing duties; holism treats competence as a cluster of elements including knowledge, skills, critical thinking ability, and values.

It has long been noted that the safety of the patients receiving medical treatments is directly related to the competence levels of health care providers, including nurses who are on the forefront of patient care in hospitals (Smith, 2012). The usage of competence as a strategy to evaluate nursing competence has been introduced and implemented since the mid-1990s (Axley, 2008), and until today, many evaluation instruments have been developed to measure the competence levels of nursing students, clinical-based nurses, and health care providers (Flinkman et al., 2017; McMullan et al., 2003; Watson, Stimpson, Topping, & Porock, 2002; Yanhua & Watson, 2011). However, few studies exist about the comprehensive analysis of the competence-influencing factors such as age, nursing title, staffing levels, education background, and work experience. In this study, we attempted to address this issue by analyzing the relationship between nurse competence levels of 160 nurses in a Chinese hospital and their nurse characteristic profiles.

Materials and Method

Participants

One hundred sixty nurses were recruited from a hospital located in the central east region of China. The participants were all RNs and involved in patient care.

Data Collection

A questionnaire method was used to collect the required information in this study. Prior to this study, a written application containing the purpose and method of this study was provided to the hospital ethical committee board, and written approval was obtained. A survey package containing a cover letter, an information form, and a questionnaire was delivered to each of the 160 nurses on April 11, 2017. The cover letter explained the purpose and method of this study. The information form collected the nurses' characteristic information. The questionnaire contained a scale to measure the nurses' competence. The finished survey package was collected after 1 week.

Nurses' Characteristic Information

The following information was collected through the information form: basic information including name, employee identification, gender, and age; work information including title (i.e., nurse, nurse practitioner, nurse in charge, co-chief nurse, chief nurse), staffing levels (N0/N1/N2/N3/N4), work years, work history (i.e., experience working in different departments or hospitals); education information (i.e., technical secondary school degree, junior college degree, university degree); and research information including patents, publications, or research projects. All categories were weighted equally in the later relationship analysis.

The staffing levels of nurses were determined using the standards (Jiang, Wen, & Xie, 2013) listed in Table 1. Corresponding training and assessments are required in the promotion of staffing levels.

Nurse Staffing Level Standards

Table 1:

Nurse Staffing Level Standards

Nurse Competence Scores

To evaluate competence levels of participating nurses, the Competence Inventory for Registered Nurses, revised from Liu, Kunaiktikul, Senaratana, Tonmukayakul, and Eriksen (2007), was used. This scale (Table 2) contained seven categories and 55 items. A 5-point Likert scale was used to rate all seven factors, ranging from 1 point (not competent at all), 2 points (rarely competent), 3 points (sometimes competent), 4 points (most of the time competent), to 5 points (always competent). Each item in all seven categories was weighted equally in the generation of final competence score. The total scores were ranged from 0 to 275 points, with higher scores indicating a stronger competence ability: 192 to 275 points (> 75%) represented a high level, 110 to 192 points (50% to 75%) represented a medium level, and <110 points (< 50%) represented a low level.

Competence Inventory Scale for RNS

Table 2:

Competence Inventory Scale for RNS

Data Analysis

SPSS 17.0 and R (version 3.5.2) software were used in collecting characteristic information and statistical analysis. Welch's t test and one-way ANOVA were used to compare the means of two or more samples. Multiple linear regression analysis was used to explore the factors effecting nursing core competency scores. A p value of .05 was used in this study.

Ethical Considerations

This study was conducted after receiving the approval from ethical committee board of the hospital, and participants were provided with information regarding the research purpose and methods in the cover letter.

Results

Summary of the Participating Nurses' Characteristics

The characteristics of 160 participating nurses are summarized in Figure 1. Among these 160 nurses, seven (4.4%) were men and 153 (95.6%) were women. Regarding to age, 119 (74.4%) were ages 20 to 30 years; 37 (23.1%) were ages 31 to 40 years; four (2.5%) were older than 40 years; the average age was 28.3 years, with a range of 20 to 47 years. Regarding titles, 90 (56.3%) were nurses; 61 (38.1%) were nurse practitioners; nine (5.6%) were nurses in charge. Regarding working years, 69 (43.1%) had 1 to 5 years of work experience; 63 (39.4%) had 6 to 10 years; and 28 (17.5%) had more than 10 years. Regarding education levels, 32 (20%) had technical secondary school degrees; 85 (53.1%) had junior college degrees; and 43 (26.9%) had university degrees. Regarding staffing levels, 13 (8.1%) were at the N0 level; 65 (40.6%) were at the N1 level; 67 (41.9%) were at the N2 level; and 15 (9.4%) were at the N4 level. Regarding work experience, 56 (35%) had only one department and one hospital experience; 47 (29.4%) had one hospital but more than one department experience; 42 (26.2%) had one department but more than one hospital experience; and 15 (9.4%) had more than one department and more than one hospital experience. Regarding research experience, two (1.3%) had research publications; 158 (98.7%) had no publications.

Summary of the characteristics of participating nurses. Seven categories, including gender, age, title, work years, education, staffing level, and work experience are listed here. Note. N0, N1, N2, N3 = staffing levels; C1, C2, C3, C4 = the experience categories of one department/one hospital, one department/more than one hospital, more than one department/one hospital, and more than one department/more than one hospital, respectively.

Figure 1.

Summary of the characteristics of participating nurses. Seven categories, including gender, age, title, work years, education, staffing level, and work experience are listed here. Note. N0, N1, N2, N3 = staffing levels; C1, C2, C3, C4 = the experience categories of one department/one hospital, one department/more than one hospital, more than one department/one hospital, and more than one department/more than one hospital, respectively.

Summary of the Competence Results of Participating Nurses

The competence scaling results of 160 participating nurses are illustrated in Figure 2. The average competence score was 158.28 points, which was at the medium level (110 to 192 points). The histograms representing score distribution are shown in Figure 2. Thirteen participants had scores lower than 110 (8.1%, low level), 113 participants had scores between 110 and 192 points (70.6%, medium level), and 34 had scores higher than 192 points (21.3%, high level). The radar map representing the average scores of participating nurses in seven subcategories are shown in Figure 2B. Among the 7 factors, participating nurses performed best in the subcategory of clinical care ability (3.96 of 5), followed by the subcategories of legal/ethical practice ability (3.68 of 5), interpersonal relationship ability (3.23 of 5), leadership ability (2.75 of 5), teaching/coaching ability (2.05 of 5), and critical thinking and research ability (1.78 of 5).

Summary of the nurse competence scaling results. (A) The bar plot showing the score distribution of participating nurses. (B) Radar plot illustrating the competence performance on seven different factors (maximum score = 5).

Figure 2.

Summary of the nurse competence scaling results. (A) The bar plot showing the score distribution of participating nurses. (B) Radar plot illustrating the competence performance on seven different factors (maximum score = 5).

Relationship Between Competence and Characteristics

To explore the relationship between nurses' competence and their characteristics, the mean competence score from each subcharacteristic category was compared with each other, as listed in Table 3. Welch's t test (on characteristics with two subcharacteristic categories: gender, research) and one-way ANOVA (on characteristics with more than two subcharacteristic categories: age, title, staffing levels, work years, education, experience) were performed to test the statistical significance of each comparison. The competence score distribution in correspondence with different characteristics are illustrated in Figure 3.

Participating Nurse Characteristics and Differences in Nurse Competence (N = 160)

Table 3:

Participating Nurse Characteristics and Differences in Nurse Competence (N = 160)

The competence score distribution in relation to different characteristics: (A) Age. (B) Nursing titles. (C) Work years. (D) Education level. (E) Staffing level. (F) Work experience. Note. UD = university degree; JCD = junior college degree; TSSD = technical secondary school degree.The competence score distribution in relation to different characteristics: (A) Age. (B) Nursing titles. (C) Work years. (D) Education level. (E) Staffing level. (F) Work experience. Note. UD = university degree; JCD = junior college degree; TSSD = technical secondary school degree.

Figure 3.

The competence score distribution in relation to different characteristics: (A) Age. (B) Nursing titles. (C) Work years. (D) Education level. (E) Staffing level. (F) Work experience. Note. UD = university degree; JCD = junior college degree; TSSD = technical secondary school degree.

Among all characteristic categories, the competence scores differed dramatically in characteristics of staffing levels and education (p < .001). In staffing level category, the mean competence score increased with the increasing of staffing levels (N3 > N2 > N1 > N0). In education level category, the mean competence score increased with the increasing of education levels (university degree > junior college degree > technical secondary school degree). The mean competence scores also differed in characteristic categories of title (p = .011), where only the mean competence score of nurses with the title of nurse in charge was higher than the mean score of nurses with the title of nurse.

The competence score distribution in correspondence to staffing levels and education levels are also illustrated in Figure 3. In the education level category, all nurses with university degrees had scores located in medium and high regions, and nurses with technical secondary school degrees (TSSD) had scores locating more to the left. In the staffing level category, all nurses at level N0 had scores located in the low and medium regions, and all nurses at level N3 had scores in the medium and high regions. Nurses at levels N1 and N2 had scores located across all three regions, with level N1 located more toward the left and level N2 located more toward the right. All these were consistent with previous one-way ANOVA analysis.

The variables that were significant in t test and oneway ANOVA analysis were further used in the following formula: Lm (Score = Age + Title + Staffing level + Education) to perform multiple linear regression analysis, and the results are provided in Table 4. Among them, subcharacteristics title (nurse in charge), staffing levels (N1/N2), and education (TSSD/JCD) had p values < .05, and these variables together explained 33% of the variance in nursing core competencies.

Factors Influencing Nursing Competence, Analyzed by Multiple Regressiona

Table 4:

Factors Influencing Nursing Competence, Analyzed by Multiple Regression

Discussion

As of 2018, approximately 3.8 million RNs serve 1.4 billion people in China (data retrieved from Chinese nursing website: http://www.chinanurse.cn). The nursing system in China is different from that in in other countries in many ways:

  • Five levels of nurse titles exist from low to high, including nurse, nurse practitioner, nurse in charge, co-chief nurse, and chief nurse, which is different from the four-level nurse title system in the United States, which includes certified nursing assistant, RN, licensed practical nurse, and advanced practice registered nurses.
  • Five levels of nurse staffing levels (N0, N1, N2, N3, N4) exist, as described in the Materials and Methods section. This system is similar to the classification of five stages in clinical competence described by Benner (2001). There are specific trainings and assessments in each level.
  • Three main levels of nursing education programs exist, including technical secondary school program (4 years), junior college program (3 years), and university program (4 years). Nurses with master's or PhD degrees are rare.
  • There are 3 levels of hospital systems, including level 1 hospitals (community hospital), level 2 hospitals (county hospital), and level 3 hospital (city, provincial, national hospital). The hospital involved in this study is a level 3 hospital. As of 2017, the physician/nurse ratio is 1:1.46 in level 2 hospitals and 1:1.54 in level 3 hospitals, whereas this number was 3.33 in the United States (data retrieved from American Medical Association website: http://www.ama-assn.org). Hence, there is still a large demand for RNs in China (Xu, Wu, Zhang, Ma, & Li, 2016). Aside from that, more complex and sophisticated nursing requirements have been raised by patients. In short, quantity and quality are two urgent issues need to be addressed in the modern Chinese nursing system.

Over the past several decades, many instruments have been designed to scale nurse competence, with subcategories ranging from four to 10 dimensions and items ranging from 18 to 108 items (Cowan, Jenifer Wilson-Barnett, Norman, & Murrells, 2008; Liu et al., 2007; Meretoja, Isoaho, & Leino-Kilpi, 2004). In this study, the Competence Inventory for Registered Nurses, with seven dimensions and 55 items, was used in this study because this scaling tool was specially designed for the Chinese nursing system (Liu et al., 2007). The competence results indicate that the competence levels of participating nurses were at a medium level (158.28/275), and among seven different subcategories, the participating nurses had the best performance in clinical care ability category (3.96 of 5) and the worst performance in critical thinking/research ability (1.78 of 5). The lack of critical thinking and research ability in nurse competence evaluation has been reported in many countries and districts, such as in Taiwan (Chang, Chang, Kuo, Yang, & Chou, 2011), Japan (Takase & Teraoka, 2011), and Europe (Cowan et al., 2008), and our results are consistent with these previous reports. Critical thinking skill is an important part of nurse competence, in addition to mechanical skill (Bechtel et al., 1999), and the lack of such ability has become the bottleneck restricting nurse competence. The incorporation of critical thinking training into nursing education programs such as simulation exercises might help solving this issue, as suggested by Von Colln-Appling and Giuliano (2017).

The relationship between nursing characteristics and competence was also explored in this study, and among all the characteristic categories, education and staffing levels were two main influencing factors, as shown in Tables 34 and Figure 3. Nurses with higher education degrees performed better than nurses with lower education degrees, and nurses from higher staffing levels performed better than these from lower staffing levels. It has long been reported that education and staffing levels greatly influence nursing quality. Milligan (1998) suggested that nurse training, an important factor judging staffing levels, together with nurse education, were two key factors in the implementation of competence. Aiken et al. (2011) found that nurse staffing and education levels were directly related to better patient outcomes. Regarding other characteristics, although they did not show strong relationships with the results of nurse competence in this study, other reports showed they could affect nurse competence. For example, Bahreini et al. (2011) found that nursing titles had an influence on competence scaling results. Takase (2013) found that the length of nurses' clinical experience or working years also affected nurse competence.

Given that education and staffing levels are two main constraints, these two factors deserve more attention in the improvement of nurse competence levels. From nurse employers' perspectives, education background should have more weight during the recruitment of new nurses. Currently, no significant difference exists in the compensation for nurses with different education backgrounds, and a more educated nurse workforce is not necessarily more costly for hospitals. Moreover, employers should provide more options for existing nurses to improve their education levels, such as encouraging nurses to pursue a part-time nursing degree and using work-based learning strategies (Flanagan, Baldwin, & Clarke, 2000). From a nursing management perspective, more training programs should be offered to nurses from different staffing levels, and nurses from different levels can be mixed to perform certain tasks where higher level nurses can lead the team to provide higher quality nursing service. From nursing educators' perspectives, competence-based education should be provided to nursing students to ensure that graduates have the essential knowledge, skills, and attitudes to enter the nursing workforce (Pijl-Zieber, Barton, Konkin, Awosoga, & Caine, 2014). From nurses' perspectives, although going back to school can sometimes be an intimating idea, a higher education degree is not only a trend but also a necessity. Nurses can also take advantage of modern technologies such as online courses (Wu, Chan, Tan, & Wang, 2018) to gain more knowledge and improve their education levels.

Conclusion

China is a fast-developing country, with 1.4 billion people accounting for 18% of world's population. The exploration of Chinese nurse's competence status has important implications and can greatly promote the nursing studies worldwide. In this study, the nursing competence status in a Chinese hospital was explored, as well as the relationship between competence and nurse characteristics. Education and staffing levels are found to be two influencing factors of nurse competence. This study also addressed how to improve nurse competence from different perspectives, such as those of nurses, nursing employers, managers, educators, and students.

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Nurse Staffing Level Standards

LevelTitleExperienceRequirements
N0Nurse0–1 years

Completing nursing task under supervision

Certain written and communication skills

N1Nurse1–5 years

Better skills in general and specialized nursing

Ability to find nursing-related problems

Ability to provide nursing care to critically ill patients under supervision

Better written and communication skills

Certain teaching ability

Nurse practitioners1–3 years
N2Nurse> 5 years

Excellent skills in general and specialized nursing

Ability to solve problems using nursing procedures

Ability to provide nursing care to critically ill patients independently

Excellent written and communication skills

Active involvement in nursing research and teaching

Nurse practitioners> 3 years
N3Nurse practitioner> 5 years

Ability to solve nursing related problems using evidence-based methods

Ability to provide nursing care to patients with difficult diseases independently

Supervising ability in specialized nursing care

Excellent written and communication skills

Certain research and creativity ability

Certain management ability

N4Co-chief nurse and aboveNot applicable

Proficient in general and specialized nursing care

Ability to complete high-demanding and difficult nursing tasks independently

Strong teaching ability

Ability to solve problems using comprehensive methods

Strong research ability

Strong management ability

Competence Inventory Scale for RNS

CategoryNo. of ItemsAbility Assessed
18Critical thinking and research ability
210Clinical care ability
39Leadership ability
48Interpersonal relationship ability
58Legal/ethical practice ability
66Professional development ability
76Teaching/coaching ability

Participating Nurse Characteristics and Differences in Nurse Competence (N = 160)

Variable and SubcharacteristicNMeanSDp
Gender.54
  Male7150.2324.64
  Female153159.0538.79
Age (years).21; eta-squared = 0.019
  20–30119155.4335.2
  31–4037166.0246.51
  >404175.8535.69
Title.011; eta-squared = 0.056; nurse in charge > nurse*
  Nurse90150.8234.92
  Nurse practitioner61165.0540.58
  Nurse in charge9182.7839.83
Staffing level< .001; eta-squared = 0.21; N3 > N2*; N3 > N1**; N3 > N0**; N2 > N1*; N2 > N0**; N1 > N0*
  N013117.0228.21
  N165150.4534.24
  N267166.8933.79
  N315192.2441.08
Work history (years).12; eta-squared = 0.026
  1–563151.2536.98
  6–1069163.7837.1
  >1028160.6542.02
Education level< .001; eta-squared = 0.22; UD > JCD**; UD > TSSD**; JCD > TSSD**
  Technical secondary school32132.7630.89
  Junior college85155.0236.38
  University43184.3131.26
Work experiencea.25; eta-squared = 0.026
  Category 156151.9235.58
  Category 247167.2537.39
  Category 342156.3143.15
  Category 415159.7933.48

Factors Influencing Nursing Competence, Analyzed by Multiple Regressiona

Variable and SubcharacteristicEstimateSEt Valuep Value
Title
  Nurse in charge−34.2917.46−1.96< .001**
  Nurse practitioner1.866.170.31.051
Staffing level
  N133.519.543.51.76
  N241.7910.314.06< .001**
  N385.4716.155.29< .001**
Education level
  Technical secondary school degree−26.636.59−4.04< .001**
  University degree18.436.612.79.0059*
Authors

Mr. Jing is Chief Nurse, Ms. Sun is Chief Nurse, Laoshan Hospital Endoscopic Diagnosis and Treatment Center, Affiliated Hospital of Qingdao University, Qingdao, Ms. Zhang is Chief Nurse, Department of Nursing, Liaocheng People's Hospital, Liaocheng, Ms. Chi is Director, Nursing Department, and Mr. Lv is Assistant Director, Nursing Department, Department of Cardiology, Yantai Hospital of Traditional Chinese Medicine, Yantai, Shandong Province, China.

This study was supported by the Traditional Chinese Medicine Science and Technology Development Program of Shandong Province (No. 2017-387). The authors thank Jihua Wang, RN, and Nining You, RN, in the support of data collection, as well as Dr. Shaowei Dong's team for providing statistical support in preparing the manuscript.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Shuguang Lv, RN, Assistant Director, Nursing Department, Department of Cardiology, Yantai Hospital of Traditional Chinese Medicine, No. 39 Xingfu Road, Zhifu District, Yantai, Shandong Province, China, 264100; e-mail: shuguanglv1005@163.com.

Received: January 18, 2019
Accepted: July 17, 2019

10.3928/00220124-20191115-09

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