Effects of self-directed learning on clinical competence and the mediating role of clinical learning environment among internship nursing students: A structural equation modeling approach

DOI: https://doi.org/10.21203/rs.3.rs-2109644/v1

Abstract

Introduction

It is crucial to recognize the factors affecting the clinical competence of internship nursing students. This study aimed to determine the effects of self-directed learning on the clinical competence of internship nursing students with the mediating role of the clinical learning environment.

Methods

This cross-sectional research was performed on 300 internship nursing students selected by convenience sampling. Data were collected in one stage using three tools of Self-Directed Learning Readiness Scale for Nursing Education, Education Environment Measure, and Clinical Competence Questionnaire. Data analysis was performed in SPSS version 21 and Smart-PLS version 3 using Partial least squares-SEM with considering a p-value of 0.05.

Results

The results showed that 20.5% of the variance in clinical competence could be explained by self-directed and clinical learning environments. Self-directed learning had a positive and significant effect on clinical competence (path coefficient = 0.14, 95% CI: 0.02, 0.26; p = 0.027), and on clinical learning environment as well (path coefficient = 0.41, 95% CI: 0.31, 0.52; p < 0.001). A relationship was reported between clinical learning environment and clinical competence (path coefficient = 0.38, 95% CI: 0.25, 0.50; p < 0.001). The indirect effect of self-directed learning on clinical competence was positive and significant (path coefficient = 0.11, 95% CI: 0.07, 0.17; p < 0.001). Self-directed learning has a significant total effect on clinical competence (path coefficient = 0.30, 95% CI: 0.19, 0.40; p < 0.001).

Conclusions

It is recommended that nursing education managers and instructors consider some plans to enhance self-directed learning among nursing students and improve the clinical learning environment.

Introduction

The education and clinical competency of nurses, who make up the largest health workforce and play a role in complicated decisions and delegation of care, are important factors for improving client outcomes (Coyne et al., 2021). The term “competence” is derived from the Latin word “Competentia” and is interpreted as capability and permission. Later, the term competency and its use were defined in the nursing field by Benner (1984), where nursing competency was recognized as the ability to do a task such that favorable results are achieved (Hailu et al., 2021). Clinical competence (CC) refers to clinical capabilities required by nurses in a clinical environment to successfully carry out tasks based on knowledge, techniques, attitudes, and performance (Kang et al., 2021).

Given that the end goal of nursing education is to train competent nurses and ensure the provision of high levels of patient care, the most important goal of clinical education in nursing is to improve nursing students’ practical skills and clinical qualifications (Rafii et al., 2019). CC development for optimal nursing performance occurs throughout the nursing education process (Alavi et al., 2022). Therefore, clinical education constitutes more than half of formal nursing education courses (Jamshidi et al., 2016).

Internship in clinical education happens in the last year of nursing education as an important approach used to reduce the gap between theoretical and practical knowledge. The program was first used in the late 19th century in the United States to prepare medical students for clinical practice following graduation and to establish maturity in these individuals (Ahmadi et al., 2020). Iran’s undergraduate nursing education program is a four-year course that ends with an internship program. In this program, every year includes two semesters. In the first semester, students learn the theoretical foundations of basic nursing skills in the classroom and practice these skills in the clinical skills center. Along with theory courses, internships in clinical positions start from the second semester and nursing students do their clinical training under the direct supervision of a nursing instructor (from their faculty). The theoretical requirements of nursing education are completed by the end of the third academic year (sixth semester), and in the fourth year (seventh and eighth semesters), students complete the internship course independently and with the direct supervision of supervisors and clinical staff and indirect supervision of supervisors in the form of internships in most clinical departments (Aghaei et al., 2021).

The internship’s goal is to equip students with the necessary professional skills, prepare them for becoming competent nurses and enable them to use theoretical knowledge in practice. In fact, the program helps nursing students work as actual nurses in order to improve their clinical skills and achieve CC (Aghaei et al., 2021). Nonetheless, various factors affect the acquisition of CC, including the educational environment in the ward, the supervisory relationship between students and instructors, and preparation for performance based on nursing education (Getie et al., 2021).

Conceptual framework for hypothesized model

A major goal of all nursing education programs is to provide high-quality learning experiences that result in the growth of CC in nursing students before practice. In general, CC includes comprehension of knowledge, clinical, technical, and communication skills, and the ability to solve problems through the use of clinical judgment (Taylor et al., 2020). In a review study, CC in nursing education was defined in the form of three themes: 1) professional performance with a care perspective, 2) clinical skills and reflective performance, and 3) cognitive, emotional and psychomotor skills combined with a nursing perspective (Lejonqvist et al., 2016).

Based on the literature, the internship program helps nursing interns demonstrate leadership skills in problem-solving, prioritization, decision-making, the delegation of tasks, and accountability by trainers (Aboshaiqah and Qasim, 2018). On the other hand, since there is generally a positive relationship between clinical performance and preparation for self-directed learning (SDL) (Choi and Jeong, 2011), this type of learning is a necessary and effective strategy for the learning of nursing students in the clinical course (Noh and Kim, 2019).

SDL is defined as “A process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes” (van Lankveld et al., 2019). SDL enables individuals to self-assess their learning needs, frame their learning objectives, find resources, implement their learning strategy, and evaluate their learning outcomes (Kang et al., 2021). A self-directed learner takes control over their learning and accepts the autonomy of learning what is important to them. Researchers have found a relationship between higher preparation for SDL and better academic achievement. Therefore, greater preparation for SDL may affect nursing competence (Yang and Jiang, 2014). Many faculty members are drawn to the SDL approach owing to its ability to develop independent learning skills, as well as accountability, responsiveness, and decisiveness, which are important features throughout the profession, in nursing students, and improve their adjustment to the clinical environment. Accordingly, SDL is a necessary and effective approach for nursing students in the clinical period (Noh and Kim, 2019).

Overall, improvement of the learning environment, in which students could have a sense of motivation and participation, can help develop self-learning (Visiers-Jiménez et al., 2021). The clinical workplace is an exciting and dynamic learning environment, and such an environment requires students to develop new competencies, including SDL techniques (Embo et al., 2010). A clinical learning environment (CLE) refers to a clinical workplace in which students of health professions complete their clinical work as part of their education (Sellberg et al., 2021). According to a concept analysis study by Flott and Linden, CLE in nursing education has four components of physical space, psychosocial and interaction factors, organizational culture, and teaching and learning components (Flott and Linden, 2016). The learning environment plays a vital role, especially during the clinical education of nursing students, mainly because of facing their real performance and helping them form their viewpoints about professional jobs and perspectives of the clinical field (Papathanasiou et al., 2014). In other words, clinical learning environments play a basic role in the growth of students’ professional identity and competencies (Immonen et al., 2019).

Literature Review

Many nursing scholars have committed to studying the factors that may affect students’ CC in order to improve this issue in undergraduate nursing students (Yu et al., 2021). For instance, Yang and Jiang (2014) concluded a study to evaluate the relationship between SDL and CC in nursing undergraduates. Similarly, Choi and Jeong (2011) assessed the effect of readiness for self-directed learning on nursing practice competence. Moreover, Alotaibi (2016) attempted to determine the relationship between SDL readiness and the academic performance of students with the mediating role of understanding learning environment needs. In addition, Hwang and Oh (2021) studied the relationship between SDL and problem-solving ability with the mediating role of academic self-efficacy and self-regulated learning among nursing students. Another study was performed to evaluate the relationship between CLE and students’ competence levels (Visiers-Jiménez et al., 2021). Yu et al. (2021) carried out a study to assess the relationship between the CC of graduated nursing students and CLE.

Given the significance of assessing the factors affecting students’ CC and based on the studies mentioned above, the current research aimed to determine the effect of SDL on internship nursing students’ CC with the mediating role of CLE in the form of hypothesis model testing (Fig. 1). Considering the study by Alotaibi (2016), Yu et al. (2021), and Visiers-Jiménez et al. (2021) the following hypotheses were tested in the current research:

H1: SDL directly affects CC.

H2: SDL directly affects CLE.

H3: CLE directly affects CC.

H4: SDL indirectly affects CC.

H5: SDL generally affects CC.

Methods

Design

This cross-sectional study was performed to evaluate the effect of SDL on CC with the mediating role of CDL by using structural equation modeling (SEM) at Shahid Beheshti University of Medical Sciences (SBMU) in Tehran, Iran during 2020–2021.

Participants and setting

The participants included nursing students undergoing the internship program in the 7th and 8th semesters at SBMU. Titles of clinical courses in the seventh semester of nursing education were medical-surgical nursing (eight credits) and critical care nursing (three credits). In addition, the titles of courses in the eighth semester were emergency nursing care (two credits), maternal health care (two credits), pediatric nursing (two credits), community nursing care (two credits), and nursing management (two credits). With the exception of community nursing care, training for the rest of the clinical courses was provided in various departments of hospitals affiliated with SBMU.

SBMU annually accepts 220 applicants to study nursing in the national centralized annual exam for the fall and spring semesters (100–120 per semester). Therefore, every year, between 200 and 240 nursing students are studying in the last year of nursing education (i.e., the 7th and 8th semesters). In this study, for two consecutive years, 344 internship nursing students who were in their final year of study and were undergoing the internship course, were included in the study. Following eliminating incomplete tools, a total of 300 internship nursing students were enrolled in the study. In fact, the tool completion rate was reported at 87%.

Instruments

Demographic information questionnaire

In this study, a demographic characteristics questionnaire was used to assess five criteria of age, gender, mean grade point average, nursing work experience as a student job, and duration of employment.

Self-Directed Learning Readiness Scale for Nursing Education

At this stage, the Self-Directed Learning Readiness Scale for Nursing Education was used to measure SDL. The tool was introduced by Fisher et al. in 2001 for the first time with 40 phrases. In 2010, Fisher and King conducted a psychometric assessment of the tool, which led to the extraction of a 29-item tool with three subscales of self-management (10 items), desire to learning (9 items), and self-control (10 items). The items are scored based on a five-point Likert scale from completely agree (score 5) to completely disagree (score 1). Notably, items 2, 15, and 21 are scored reversely (Fisher and King, 2010). In addition, the lowest and highest mean scores of the tool are one and five, respectively. It is also worth noting that the reliability and validity of the Farsi version of the tool were assessed by Nadi and Sadjadian for medical and dental students, and all three subscales had high internal consistency coefficients. The Cronbach’s alpha, the Spearman-Brown coefficient, Guttman scale, and retest coefficient were reported at 0.913, 0.899, 0.898, and 0.861, respectively, all of which confirmed the reliability of the tool (Nadi and Sadjadian, 2011).

Undergraduate Clinical Education Environment Measure

At this stage, Undergraduate Clinical Education Environment Measure was applied to measure CLE. The measure was first designed at Lund University in 2012 based on theories of experiential learning and social participation. The tool encompasses 25 items and four subscales of preparedness for student entry (6 items), opportunities to learning in and through work and quality supervision (11 items), workplace interaction patterns and student inclusion (6 items), and equal treatment (2 items). The items are scored based on a five-point Likert scale from completely disagree (one score) to completely disagree (five scores). A higher score is indicative of a higher quality of educational environment (Strand et al., 2013). In addition, the lowest and highest mean scores of the tool are one and five, respectively.

In 2015, Abbasi et al. (2016) carried out the psychometric assessment of the Farsi version of the instrument, and the results confirmed the tool’s reliability at a Cronbach’s alpha of 0.93. The construct validity of the tool was evaluated by exploratory factor analysis and Pearson’s correlations, and the four factors above were extracted based on the original version. Therefore, the reliability and validity of the tool were approved.

Clinical Competence Questionnaire

The Clinical Competence Questionnaire, which is used to measure CC in internship nursing students, was first developed by Cheng and Liou in 2014. The instrument includes 46 items and four subscales of nursing professional behaviors (16 phrases), skill competence: general performance (12 phrases), skill competence: core nursing skills (12 items), and skill competence: advanced nursing skills (6 items). The items are scored based on a five-point Likert scale, as shown follows: “Do not have a clue” (score 1), “Know in theory, but confident at all in practice” (score 2), “Know in theory, can perform some parts in practice independently, and needs supervision to be readily available” (score 3), “Know in theory, competent in practice need contactable sources of supervision” (score 4), “Know in theory, competent in practice without any supervision” (score 5). In this tool, a higher score is indicative of greater CC (Liou and Cheng, 2014). Moreover, the lowest and highest mean scores of the tool are one and five, respectively. This Clinical Competence Questionnaire was translated and psychometrically assessed in Iran in this research for the first time. After translation and ensuring consistency between two translations, the tool was provided to 10 faculty members of the nursing and midwifery school to assess qualitative validity and the necessity of questions by using the content validity ratio formula. According to the suggested values of Lawshe’s table and scoring more than 0.64 for each of the questions, all of the items were kept in the tool. Moreover, the relevance of the questions to the purpose of the questionnaire was also checked using the content validity index (Vasli, 2018) and its total mean was 0.99. Therefore, all the questions were confirmed in terms of relevance.

At this stage, the internal consistency method (Cronbach’s alpha) was used to confirm the reliability of the tools. To this end, all three tools were completed by 20 eligible internship nursing students who were not included in the research. In the end, reliability of the Self-Directed Learning Readiness Scale for Nursing Education, Undergraduate Clinical Education Environment Measure, and Clinical Competence Questionnaire was confirmed at a Cronbach’s alpha of 0.92, 0.88, and 0.96, respectively.

Data Collection

The data was collected at one stage and at the place of access to internship nursing students, which were different departments of hospitals affiliated with SBMU. The corresponding author of the study, who was the planner and supervisor of internship nursing students and directly interacted with them, collected data with the help of the second researcher over four academic semesters and in the morning and night shifts. In this respect, the tools were distributed among the students, and they were asked to complete and return them as soon as possible.

Data Analysis

In this study, the partial least squares-SEM (PLS-SEM) was used to test the hypothetical research model. The basis of PLS-SEM is a regression technique that, in addition to exploring the linear relationship between several independent variables and one or more dependent variables, measures the relationship networks between structures as well as the relationship between structures and their measures. The basis of model testing in SEM-PLS is to determine the fit of the measurement model and the structural model, and the data analysis was performed after obtaining assurance of the suitability of the two mentioned fits. The measurement model examines the assumed relationships between indicators and latent structures, while the structural model evaluates the assumed paths between endogenous latent variables and exogenous latent variables (Vinzi et al., 2010).

Measurement Model

The following steps were taken in the measurement model: indicator reliability, internal consistency reliability, convergent validity, and discriminant validity (Hair et al., 2021a).

The first step (indicator reliability) determines how much of the variance of each indicator is explained by its construct and is performed by indicator loading. Indicators with values less than 0.4 should be removed, and indicators from 0.4 to 0.708 should be considered for removal only when the removal of the index leads to an increase in internal consistency reliability or convergent validity with values higher than the threshold (Hair et al., 2021a).

The two measures of composite reliability and Cronbach’s alpha were used to assess internal consistency reliability, which shows the relationship between variables. For both measures, values 0.6–0.7, 0.7–0.9, and > 0.9 were considered “acceptable”, “satisfactory to good” and “problematic”, respectively (Hair et al., 2021a). Convergent validity as a third step is the extent to which the construct converges in order to explain the variance of its indicators. The metric used to evaluate convergent validity is the average variance extracted (AVE) for all indicators in each structure, and its minimum acceptable value is 0.5 (Hair et al., 2021a).

Discriminant validity as the fourth stage measures the degree of empirical differentiation of a construct from other constructs in the structural model. The foregoing concept can be assessed by three methods of Fornell-Larcker, cross-loading, and heterotrait-monotrait ratio of correlations (HTMT). In Fornell-Larcker, the square root of AVE is compared with the correlation of hidden variables and its value should be higher than the correlation of construct with hidden variables (Hair et al., 2021a). In examining cross-loadings, discriminant validity is shown when each indicator has a weak correlation with all other constructs except the construct that is theoretically related to it. The HTMT is defined as the mean value of the indicator correlations across constructs relative to the mean of the average correlations for the indicators measuring the same construct, and its value must be below 0.85. A value above 0.9 is indicative of a lack of discriminant validity in the path model (Henseler et al., 2015).

Structural Model

The structural model in PLS-SEM is evaluated by focusing on evaluating the significance and relevance of path coefficients, followed by the model’s explanatory and predictive power. Moreover, significance assessment is carried out by calculating the t-value for path coefficients. In terms of relationship, path coefficients are normally between − 1 and + 1, coefficients closer to -1 indicate strong negative relationships and coefficients closer to + 1 indicate strong positive relationships. The next stage is the coefficient of determination (R2) related to endogenous construct(s). in general, R2 varies from zero to 1, and higher values are indicative of higher explanatory power (Hair et al., 2021b). Considering that the basic assumption for analyzing with the SEM method is the normality of the data, the normality of the data was first performed using Kolmogorov–Smirnov test. In addition, data analysis was performed in SPSS version 21 to describe demographic characteristics and assess the main variables in terms of mean and standard deviation, and Smart-PLS was applied for path analysis of research variables.

Ethical Approval

All the procedures performed in this study were approved by the Ethics Committee of XXX (the names of university, city, country, and ethical code), in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments or comparable standards. Informed consent was also obtained from all the study participants. 

Results

Demographic characteristics 

According to Table 1, the study included 300 participants (166 females and 134 males) with a mean age of 23.33 ± 2.10 years. Ninety-seven percent of the cases occurred during terms seven (20.3%) and eight (76.7%). Students had a mean grade point average of 16.92 ± 1.04. There were 120 students who held a job for a period of 6.31 ± 6.34 months.

Measurement Model

As shown in Table 2, all constructs had Cronbach's alpha values above 0.7, and the composite reliabilities exceeded 0.7 demonstrating internal consistent reliability. The AVEs were above 0.5, indicating convergent validity. 

The square root of the AVE (Fornell and Larcker Criterion) was greater than its correlation with all other constructs (Table 3). As shown in Table 4, cross-loadings have higher loadings on the parent construct than on other constructs in the model, indicating discriminant validity. HTMT values below 0.90 (Table 5). All three values mentioned above proved discriminant validity. 

Structural Model

The structural model is shown in Fig. 2 with standardized coefficients (path coefficients) and R2. According to R2, 20.5% of the variance in CC can be explained by independent variables including SDL and CLE. In addition, 17.0% of the variance in CLE was explained by SDL. The p-values for each path coefficient are shown in Fig. 3. The standardized effects of SDL and CLE on CC were significant.

According to Table 6, SDL had a positive and significant effect on CC (path coefficient=0.14, 95% CI: 0.02, 0.26; p=0.027). It means a higher score of SDL was associated with a higher score of CC. Therefore, H1 can be accepted. SDL had a significant effect on CLE as well (path coefficient=0.41, 95% CI: 0.31, 0.52; p<0.001). As a result, H2 can be accepted. It was observed that CLE was related to CC (path coefficient=0.38, 95% CI: 0.25, 0.50; p<0.001). Accordingly, higher values of CLE were related to higher values of CC. Therefore, H3 is accepted. The indirect effect of SDL on the CC was positive and significant (path coefficient=0.11, 95% CI: 0.07, 0.17; p<0.001), and thus H4 is accepted. SLD has significant total effect on CC (path coefficient=0.30, 95% CI: 0.19, 0.40; p<0.001). 

Discussion

The five hypotheses of the research were confirmed, and the results indicated that SDL affected CC and CLE. In addition, there was a relationship between CLE and CC of internship nursing students. SDL also affected CC indirectly. As a more general result, it could be stated that SDL was related to internship nursing students’ CC, and CLE played a mediating role in this respect. 

Other studies have yielded similar results regarding the first, fourth, and fifth hypotheses. For instance, Noh and Kim (2019) evaluated the effect of SDL programs on nursing students’ CC using blended coaching. In the end, their results demonstrated the effectiveness of the intervention. According to Yang and Jiang (2014), preparedness for SDL had a direct and strong association with nurses’ competence. In a study by Park et al. (2016), there was a relationship between SDL and nurses’ competence along with other factors. In another research, the results demonstrated an increase in nursing students’ problem-solving ability with the mediating role of academic self-efficacy and SDL (Hwang and Oh, 2021). Moreover, Peck et al. (2014) reported the positive impact of self-direct modules on the acquisition of practical capabilities in physiotherapy students before graduation. Furthermore, Alotaibi (2016) realized that preparedness for SDL positively affected nursing students’ academic performance. Similarly, Choi and Jeong (2011) evaluated the relationship between SDL preparedness and last-year nursing students’ nursing performance competence in China, reporting a direct relationship between the variables. Considering that the above findings confirm that SDL is student-centered learning that leads to the identification of learning gaps in skills and setting goals for learning (Charokar and Dulloo, 2022), it could be expected that it will increase the possibility of identifying educational and learning needs in the clinical environment and acquiring CC.

The second research hypothesis was also confirmed, and the results showed the effect of SDL on CLE. A literature review demonstrated that very limited studies have been conducted on the relationship between SDL and CLE. In the research by Alotaibi (2016), similar results were achieved by SEM, and the results showed the positive impact of preparedness for SDL on nursing students’ perception of CLE. A review study was conducted to evaluate SDL in a clinical environment. According to the results, SDL has yet to achieve its full potential in clinical environments (van Houten‐Schat et al., 2018). This could be justified by the fact that since the process is student-centered and allows them to have autonomy and internal motivation for learning, its use increases the possibility of benefiting nursing students from CLE and upgrading CC.

The results related to the third hypothesis showed a relationship between CLE and CC of internship nursing students. In line with our findings, Visiers-Jiménez et al. (2021) demonstrated a positive relationship between CLE and graduated nursing students’ competence. A similar study was conducted in China by Yu et al. (2021), the results of which illustrated a relationship between CLE and CC. Considering that CLE is an interactive network of all factors affecting the learning outcomes of nursing students in a clinical environment (Yu et al., 2021), everything that exists in the clinical departments can somehow affect the CC of the internship nursing students. Based on this point of view, all-around attention should be paid to the improvement of this environment to enhance the CC of the students.

Conclusion

According to the researchers of the current research, the findings could be unique, valuable, and applicable since they focused on the CC of internship nursing students and because the nursing internship course is relatively new in Iran. Our findings revealed a positive relationship between SDL and increased CC in internship nursing students. In addition, CLE helped boost this relationship. Therefore, SDL and CLE are two important factors for CC improvement in internship nursing students. Therefore, it is recommended that SDL reinforcement be considered in nursing educational programs, as well as theoretical and clinical courses, and before the entrance of students into internship programs. This would encourage nursing instructors to develop this type of learning so that they could benefit from it in CC acquisition during the internship program. Moreover, it is suggested that CLE improvement measures be taken; for instance, clinical spaces and wards should be considered for clinical education of internship nursing students, where educational facilities, as well as organizational culture with interaction and based on education, are provided to nursing students. Another measure could be empowering nursing instructors, mentors, and preceptors in order to properly interact with nursing students. Some of the major drawbacks of the present study were the number of items in the tools, self-report and low motivation of the participants in completing the instruments, which might have affected the results. Even though the present study was conducted in Iran, its results could be generalized to the nursing education programs of other countries, especially those that offer residency and internship programs. 

Declarations

Acknowledgments

The authors hereby extend their gratitude to all students who took part in the study, willing shared their beliefs and experiences.

Funding Source

Not applicable.

Conflict of Interest

The authors have no conflict of interest to declare.

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Tables

Table 1. Descriptive statistics of participants 

Variables

Frequency (%)

Gender


Female

166 (55.3)

Male

134 (44.7)

Term


Seven

70 (23.3)

Eight

230 (76.7)

Student job


Yes

120 (40.0)

No

180 (60.0)

Table 2. Indicator reliability, construct reliability and convergent validity testing results

Construct

Item

Standardized factor loading

Internal consistency validity 

Average Variance

Extracted (AVE)

Composite reliability

Cronbach’s

CC

CC17

0.81

0.94

0.94

0.51

CC18

0.79

CC19

0.70

CC20

0.72

CC21

0.74

CC23

0.78

CC24

0.76

CC25

0.71

CC26

0.70

CC27

0.77

CC28

0.72

CC30

0.64

CC31

0.63

CC32

0.65

CC37

0.60

CC39

0.63

CLE

CLE1

0.75

0.96

0.96

0.52

CLE2

0.67

CLE3

0.78

CLE4

0.76

CLE5

0.75

CLE6

0.73

CLE7

0.66

CLE8

0.63

CLE9

0.67

CLE10

0.75

CLE11

0.75

CLE12

0.77

CLE13

0.81

CLE14

0.76

CLE15

0.77

CLE16

0.76

CLE17

0.77

CLE18

0.74

CLE19

0.70

CLE20

0.68

CLE21

0.72

CLE22

0.61

CLE23

0.63

CLE24

0.62

CLE25

0.76

SDL

SDL1

0.68

0.94

0.93

0.51

SDL2

0.72

SDL5

0.74

SDL8

0.75

SDL9

0.73

SDL10

0.72

SDL11

0.71

SDL12

0.71

SDL13

0.69

SDL19

0.68

SDL20

0.72

SDL25

0.75

SDL26

0.66

SDL27

0.66

SDL28

0.75

Note: SDL Self-directed learning; CLE Clinical learning environment; CC Clinical competence

Table 3. Descriptive statistics, correlation matrix and square roots of AVE (Fornell and Larcker Criterion)

Construct

Mean

SD

CC (1)

CLE (2)

SDL (3)

CC (1)

4.27

0.61

0.71



CLE (2)

2.83

0.90

0.44

0.72


SDL (3)

4.22

0.55

0.30

0.41

0.71

Note: SDL Self-directed learning; CLE Clinical learning environment; CC Clinical competence

Table 4. Loadings and cross-loadings of the items

Item

Clinical competency

Clinical learning environment

Self-directed learning

CC17

0.81

0.36

0.28

CC18

0.79

0.29

0.24

CC19

0.70

0.28

0.01

CC20

0.72

0.31

0.07

CC21

0.74

0.38

0.16

CC23

0.78

0.33

0.18

CC24

0.76

0.36

0.32

CC25

0.71

0.24

0.04

CC26

0.70

0.24

0.04

CC27

0.77

0.32

0.25

CC28

0.72

0.31

0.14

CC30

0.64

0.33

0.34

CC31

0.63

0.31

0.31

CC32

0.65

0.32

0.35

CC34

0.42

0.21

0.01

CC37

0.60

0.27

0.25

CC39

0.63

0.27

0.33

CLS1

0.31

0.75

0.37

CLS2

0.26

0.67

0.32

CLS3

0.27

0.78

0.29

CLS4

0.28

0.76

0.27

CLS5

0.28

0.75

0.29

CLS6

0.34

0.73

0.29

CLS7

0.24

0.66

0.28

CLS8

0.24

0.63

0.24

CLS9

0.30

0.67

0.27

CLS10

0.31

0.75

0.30

CLS11

0.27

0.75

0.26

CLS12

0.34

0.77

0.46

CLS13

0.36

0.81

0.27

CLS14

0.35

0.76

0.24

CLS15

0.37

0.77

0.28

CLS16

0.34

0.76

0.29

CLS17

0.32

0.77

0.31

CLS18

0.26

0.74

0.25

CLS19

0.31

0.70

0.30

CLS20

0.33

0.68

0.30

CLS21

0.38

0.72

0.44

CLS22

0.34

0.61

0.28

CLS23

0.34

0.63

0.26

CLS24

0.35

0.62

0.28

CLS25

0.34

0.76

0.28

SDL1

0.22

0.30

0.68

SDL2

0.17

0.33

0.72

SDL5

0.16

0.27

0.74

SDL8

0.18

0.36

0.75

SDL9

0.21

0.32

0.73

SDL10

0.21

0.31

0.72

SDL11

0.27

0.26

0.71

SDL12

0.28

0.25

0.71

SDL13

0.28

0.24

0.69

SDL19

0.23

0.27

0.68

SDL20

0.17

0.30

0.72

SDL25

0.25

0.30

0.75

SDL26

0.18

0.30

0.66

SDL27

0.14

0.30

0.66

SDL28

0.20

0.31

0.75

Note: SDL Self-directed learning; CLE Clinical learning environment; CC Clinical competence

Table 5. The HTMT ratio

Construct

CC

CLE

SDL

CC




CLE

0.45



SDL

0.32

0.44


Note: SDL Self-directed learning; CLE Clinical learning environment; CC Clinical competence

Table 6. Hypotheses testing results of estimates and t-values

Hypothesis

 Path

Estimate (95% CI)

t-value

p-value 

Direct effect





H1

SDL CC

0.14 (0.02, 0.26)

2.22

0.027

H2

SDL CLE

0.41 (0.31, 0.52)

7.46

<0.001

H3

CLE CC

0.38 (0.25, 0.50)

5.95

<0.001

Indirect effect





H4

SDL CLE CC

0.11 (0.07, 0.17)

4.44

<0.001

Total effect





H5

SDL CC

0.30 (0.19, 0.40)

5.31

<0.001

Note: SDL Self-directed learning; CLE Clinical learning environment; CC Clinical competence