Readiness for Practice Among Nursing College Graduates: A Cross-Sectional Correlation Study

Abstract

Introduction

Nursing college education aims to provide students with professional competence in nursing care practices (e.g., critical thinking, problem-solving, and clinical evaluation knowledge). The practice readiness of nursing college graduates is crucial for new nurses’ successful role transition.

Objective

To determine the practice readiness of nursing college graduates in Korea and explore related factors.

Methods

A cross-sectional descriptive study was conducted using convenience sampling. Participants were newly licensed nurses who graduated from 10 nursing colleges in South Korea in 2023 and had not yet begun clinical practice. Inclusion criteria were: age 20–30 years, possession of a nursing license, and informed consent to participate. Individuals with psychiatric histories or clinical work experience were excluded. The required sample size was calculated as 172; 178 participants were included. Study instruments included the Tromsø Social Intelligence Scale, the Korean Undergraduate Clinical Education Environment Measure, and the Korean version of the Readiness for Practice Survey.

Results

The overall mean score for practice readiness was 2.81 ± 0.37. Among subfactors, professional identity scored highest (2.96 ± 0.47), while trials and tribulations scored lowest (2.61 ± 0.45). Social intelligence (r = .61, p < .001) and clinical education environment (r = .53, p < .001) were positively correlated with practice readiness. In the final regression model, satisfaction with simulation practice (β = .20, P = .001), satisfaction with overall university education (β = .15, p = .041), social intelligence (β = .49, p < .001), and clinical education environment (β = .28, p < .001) were significant factors.

Conclusion

Enhancing social intelligence and satisfaction with simulation and overall educational experiences are key to improving graduates’ readiness for clinical practice. Investing in high-quality clinical environments and simulation programs may foster smoother transitions to professional nursing roles. These findings highlight the need for curriculum and policy initiatives that support individual development and systemic improvements in nursing education.

Keywords

nursing education research nursing students baccalaureate nursing education social intelligence nursing college graduates readiness for practice

Introduction

Nursing is a critical discipline within public health, and nursing students are future healthcare professionals responsible for safeguarding the health and well-being of the public. The healthcare environment is rapidly evolving, driven by factors such as an aging population, increasing patient acuity, and frequent emergence of novel infectious diseases. These shifts highlight the growing need for well-prepared nurses. In response to these societal demands, nursing education programs aim to equip students with essential professional competencies, such as critical thinking, problem-solving, and clinical evaluation skills, which are fundamental to effective practice (World Health Organization, 2020).

However, new nurses often report significant challenges when transitioning from academic training to clinical practice, underscoring the need for strategies to improve the readiness of nursing students (Mirza et al., 2019). These challenges include excessive workload, insufficient clinical skills and knowledge, inadequate interpersonal communication skills, and limited resilience (Lee et al., 2023). These difficulties are often described as “reality shock,” a phenomenon that can have detrimental effects on the physical and mental health of new nurses (Cao et al., 2021; Kramer, 1974). Prolonged exposure to reality shocks may decrease job engagement and satisfaction, ultimately increasing turnover intentions (Kim & Yeo, 2021). Given the profound impact of these challenges, it is essential to address reality shock and develop strategies to support the successful adaptation of new nurses. Recent studies have identified nursing practice readiness as a key predictor of successful adaptation to the clinical environment for new nurses (Lee et al., 2024; Mirza et al., 2019; Moore et al., 2023). Therefore, nursing graduates’ practice readiness plays a critical role in their effective transition into professional roles.

Accordingly, this study aimed to assess the practice readiness of nursing students and identify factors associated with it, hypothesizing that multiple personal and educational variables affect their readiness for clinical practice.

Review of Literature

Practice readiness in nursing refers to the extent to which a new nurse possesses the attitudes and characteristics necessary to successfully perform in a clinical setting (Kim & Shin, 2022) and the outcomes nursing students achieve through learning experiences (Lee et al., 2022). Its attributes can be broadly classified into clinical nursing competence, interpersonal skills, competencies for maintaining professionalism, and personality traits (Kim & Shin, 2022). The ability to provide safe and effective nursing care that meets the needs of patients, demonstrate effective verbal communication skills, maintain professional nursing documentation, and perform nursing interventions based on ethical and legal foundations is also included in this context (Leong & Crossman, 2015). Students with high practice readiness tend to transition smoothly into their roles as new nurses, demonstrating strong nursing knowledge and competencies and confidence in their practice (Mirza et al., 2019). Conversely, those with insufficient practice readiness may struggle to apply their learned skills and knowledge to clinical settings (Leong & Crossman, 2015), potentially leading to transition shock and ultimately resulting in anxiety, phobias, fear of interpersonal relationships, fatigue, and turnover intention (Cao et al., 2021; Duchscher, 2009). While several studies consistently report that nursing students score high on professional identity and learning techniques but show lower confidence in managing complex clinical situations (Casey et al., 2011; Rusch et al., 2019; Schmitt & Lancaster, 2019), this pattern appears consistent across different educational contexts. Addressing these challenges necessitates an investigation into practice readiness among nursing students to develop multidimensional strategies to improve it.

Social intelligence is the ability to adaptively navigate social situations by accepting and understanding others’ emotions and behaviors, including emotional intelligence (Marlowe, 1986). People with strong social abilities can accurately identify the behaviors and moods of others during social interactions and cope appropriately in different social situations, which are important skills for social adaptation (Goleman, 2006). As a psychological protective factor, social intelligence helps form interpersonal relationships and mitigates the negative effects of stress (Goleman & Boyatzis, 2008). In nursing, characterized by diverse interpersonal dynamics, social intelligence is essential for both intrapersonal and interpersonal benefits, including life satisfaction and leadership skills (Bulmer Smith et al., 2009; Wessel et al., 2008). According to Benner's novice to expert theory, the transition from nursing student to practicing nurse requires not only the accumulation of clinical experience but also the development of critical thinking, intuition, and situational judgment (Benner, 1984). Within this framework, social intelligence is viewed as a personal resource that enhances interpersonal communication, teamwork, and adaptation, which are key competencies for progressing from novice to competent practitioners.

Clinical education is pivotal within nursing curricula (Arkan et al., 2018) and provides nursing students with opportunities to develop professional characteristics, clinical judgment, critical thinking skills, confidence, and relationship-building abilities through hands-on experience (Nishioka et al., 2014). Kramer's reality shock theory explains the psychological and emotional stress that new nurses experience when the ideals learned in school confront the realities of clinical practice (Kramer, 1974). This “shock” can lead to dissatisfaction, burnout, and even early resignation if not adequately managed. From this perspective, the clinical learning environment during nursing education plays a crucial role in mitigating reality shock by aligning educational experiences with real-world clinical demands. The educational environment, encompassing physical space, psychosocial and interactive factors, instructor effectiveness, student participation, and organizational culture, directly affects on clinical educational outcomes. These factors collectively impact students’ competencies in achieving the desired learning outcomes (Flott & Linden, 2016). In Korea, “reality shock,” the stark contrast between theoretical education and clinical reality, is the main cause of turnover among new nurses, indicating the need to bridge this gap through quality enhancements in clinical education. Therefore, it is crucial to evaluate clinical education environments and develop student-centered strategies to improve practice readiness. Research on the effectiveness of simulation education shows varying results, with some studies demonstrating significant improvements in learning outcomes (Tong et al., 2022), while implementation challenges remain a consistent concern across institutions.

Recently research has been actively exploring how practice readiness can alleviate this problem among new nurses (Cao et al., 2021; Duchscher, 2009; Kim & Shin, 2022; Mirza et al., 2019). To facilitate a successful transition for nursing students into professional roles, coordinated strategic measures are necessary at both the hospital organization and college education levels (Collard et al., 2020; Lee et al., 2019; Tseng & Hwang, 2021; van Rooyen et al., 2018). Identifying the level of practice readiness and related factors among nursing college graduates can evaluate the current outcomes of nursing education in Korea. It can also highlight areas that require focus from an educational perspective, providing foundational data for developing specific strategies to enhance practice readiness. Therefore, this study aimed to assess the level of practice readiness among nursing college graduates and identify related factors, grounded in an integrated theoretical framework combining Benner's novice to expert theory and Kramer's reality shock theory. According to Benner's theory, the progression from novice to competent practitioner requires not only the accumulation of clinical experience but also the development of critical thinking, intuition, and situational judgment (Benner, 1984), competencies that are significantly enhanced through effective interpersonal communication and social adaptation skills. Within this framework, social intelligence serves as a personal resource that facilitates the development of these essential competencies by enabling students to build therapeutic relationships, collaborate effectively with healthcare teams, and navigate complex social dynamics in clinical settings (Goleman, 2006; Goleman & Boyatzis, 2008).

Simultaneously, Kramer's reality shock theory emphasizes that the gap between educational ideals and clinical reality can create psychological stress and adaptation difficulties for new nurses (Kramer, 1974). This theory suggests that supportive learning environments during education can mitigate such shocks by providing realistic preparation for clinical practice (Flott & Linden, 2016). In this context, a positive clinical learning environment serves as a protective factor that bridges the theory–practice gap and supports students’ smooth transition to professional roles.

By integrating these theoretical perspectives, this study proposes that social intelligence (as an individual factor) and the clinical learning environment (as a contextual factor) work synergistically to enhance nursing students’ practice readiness. Students with higher social intelligence are better equipped to navigate interpersonal challenges and adapt to clinical environments, while supportive educational environments provide the contextual foundation necessary to develop confidence and competence in professional nursing roles.

Methods

Design

This study was a cross-sectional descriptive study. Data were collected from June 1 to July 30, 2023. After creating an online questionnaire, which included a research description and questions, the researchers conducted a preliminary survey with 10 fourth-year students at B University, which confirmed that there were no difficulties in reading or understanding the questionnaire. Once the questionnaire was finalized, participants were recruited by creating a “research participant recruitment” post on an online message board for fourth-year students from 10 nursing colleges across Korea. As a token of appreciation, participants who completed received a small gift.

Sample

The target population comprised recent nursing college graduates in South Korea. The required sample size was calculated using G*Power 3.1, based on a moderate effect size of 0.15, as recommended by Cohen (1988) for multiple regression analysis. With a significance level of .05, power of .95, and 10 predictive variables, the minimum sample size required was determined to be 172 individuals. Based on the findings of Hoerger (2010), who reported an approximately 20% dropout rate in online surveys, the anticipated dropout rate for this study was set at 20%. In this study, 206 individuals initially responded to an online questionnaire. However, after excluding 28 participants aged 31 years or older, 178 participants (88.2%) were included in the final analysis. The online survey platform was programmed to require responses for all items before advancing to the next section, which ensured complete data for each participant. Consequently, there were no missing data in this study, and no imputation or additional data handling procedures were necessary.

Inclusion/Exclusion Criteria

The inclusion criteria were as follows: (1) individuals who graduated from a nursing college in 2023 and had received their nursing license; (2) individuals who had not begun practicing in a clinical environment; (3) individuals aged 20–30 years; and (4) individuals who received an explanation of the study purpose and procedure and voluntarily consented to participate. The exclusion criteria were as follows: (1) individuals currently diagnosed with and undergoing treatment for severe psychiatric disorders (e.g., schizophrenia, bipolar disorder, major depressive disorder) and (2) individuals working in a clinical environment.

Institutional Review Board Approval, Informed Consent, and Human Subjects’ Rights

This study was approved by the Institutional Review Board of the investigators’ institution (IRB No. WS-2023-09). Adhering to ethical principles, participants received an explanation sheet outlining the study's purpose, methods, the voluntary nature of their participation, and their right to withdraw consent at any time without facing any penalties. Informed consent was obtained electronically before participation, and all responses were collected anonymously to ensure participant privacy. Confidentiality was maintained by not collecting any personally identifiable information, and all data were encrypted and securely stored on a password-protected computer accessible only to the research team. Participants were required to complete an online consent form indicating their voluntary participation. No personally identifiable information such as names and phone numbers was collected. Data were encrypted and stored on a locked personal computer.

Research Instruments

Sociodemographic Characteristics

For sociodemographic characteristics, participants were asked about their gender, age, geographical location of their university, and final grade point average upon graduation. Additionally, this study assessed participants’ satisfaction levels regarding four key educational domains: nursing major, lectures, simulation practice, and overall university education. Satisfaction was measured using four separate items, each asking “How satisfied are you with [specific domain]?” Participants responded using a 3-point Likert scale (0 = “dissatisfied”; 1 = “not sure”; 2 = “satisfied”). These satisfaction measures were developed for this study to capture students’ subjective evaluation of their educational experiences across critical learning domains. While single-item scales have been criticized for lacking psychometric robustness, recent scholarship argues that they are appropriate for narrow, clearly defined constructs such as satisfaction (Allen et al., 2022).

Social Intelligence

This study measured social intelligence using the Tromsø Social Intelligence Scale (TSIS), developed by Silvera et al. (2001). The TSIS comprises three subfactors, social information processing, social skills, and social awareness, encompassing 21 questions rated on a 7-point Likert scale (ranging from 1 = “describes me extremely poorly” to 7 = “describes me extremely well”), and higher scores indicate higher levels of social intelligence. Sample items include “I often feel uncertain around new people” (reverse-scored, social information processing), “I am good at entering new situations” (social skills), and “I pay attention to social situations” (social awareness). The TSIS has been validated through exploratory and confirmatory factor analyses and has demonstrated good construct validity and internal consistency (Cronbach's α = .72–.85; Silvera et al., 2001). To calculate the overall mean score, the researchers summed the scores on all questions and divided the value by the number of questions. The Korean version used in this study was adapted following a standard forward–backward translation procedure. In this study, the internal consistency of the TSIS was excellent (Cronbach's α = .90).

Clinical Practical Education Environment

This study assessed participants’ clinical education environment using the Korean Undergraduate Clinical Education Environment Measure (K-UCEEM), adapted by Chun et al. (2021) and originally developed by Strand et al. (2013). Chun et al. (2021) validated and tested the reliability of the instrument for a Korean population following a standard adaptation procedure. The K-UCEEM comprises five subfactors: interactions at the practical site, equal treatment, quality of instruction in learning and clinical practice, readiness of the practical environment, and opportunities for learning participation. It includes 24 questions, each rated on a 5-point Likert scale. The overall mean score was calculated similarly to the method used with the TSIS. In the adaptation by Chun et al. (2021), Cronbach's α was .94, and it was .97 in this study.

Practice Readiness

To evaluate practice readiness, this study employed the Korean version of the Readiness for Practice Survey (K-RPS), adapted and validated by Lee et al. (2022) from the original Casey–Fink Readiness for Practice Survey (CRPS) developed by Casey et al. (2011). Lee et al. (2022) translated the CRPS into Korean and confirmed its reliability and validity through cognitive interviews and a questionnaire survey with Korean nursing students. For comparison with other studies, the researchers used the four subfactors from the original instrument: clinical problem-solving, learning techniques, professional identity, and trials and tribulations. The K-RPS comprises 20 questions, each rated on a 4-point Likert scale ranging from “strongly disagree” (1 point) to “strongly agree” (4 points). To compute the overall mean score, the researchers summed the scores on all questions and divided the value by the number of questions. Cronbach's α was .088 by Lee et al. (2022) and .89 in this study.

Statistical Analysis

Data were analyzed using the IBM SPSS 22. Sociodemographic characteristics, social intelligence, clinical practical education environments, and practice readiness were analyzed using frequencies, percentages, means, and standard deviations. Differences in practice readiness depending on sociodemographic characteristics were analyzed using independent t-tests and one-way analyses of variance, and Scheffé's test was used for posthoc analysis. Correlations between social intelligence, clinical educational environment, and practice readiness were analyzed using Pearson's correlation coefficient. To identify factors related to practice readiness in nursing college graduates, the researchers performed hierarchical regression analysis.

Results

Sociodemographic Characteristics and Differences in Readiness

Participants’ mean age was 23.85 ± 1.68 years, and 88.2% (n = 157) were women. Incheon/Gyeonggi (28.1%, n = 50) was the most common university region. At graduation, 47.2% (n = 84) had a cumulative GPA of ≥3.5 to <4.0. Further, 58.4% (n = 104) of participants were satisfied with the nursing major, 46.6% (n = 83) were satisfied with the lectures, 47.8% (n = 85) were satisfied with the simulation practice, and 43.4% (n = 77) were satisfied with their overall university education (Table 1).

Table 1.

General Participant Characteristics and Differences in Practice Readiness (N = 178).

Characteristic	Category	n (%)	Practice readiness
Characteristic	Category	n (%)	M ± SD	t or F (p)
Gender	Men	21 (11.8)	2.75 ± 0.46	−0.778
Gender	Women	157 (88.2)	2.82 ± 0.35	(.438)
Age (years)	≤23	113 (63.5)	2.82 ± 0.36	0.198
	≥24	72 (36.5)	2.81 ± 0.39	(.843)
University region	Seoul	20 (11.5)	2.67 ± 0.48	1.052(.394)
	Incheon/Gyeonggi	50 (28.1)	2.78 ± 0.29
	Chungcheong/Daejeon	28 (15.7)	2.91 ± 0.40
	Jeolla/Gwangju	49 (27.5)	2.85 ± 0.36
	Gyeongsang/Daegu	12 (6.7)	2.80 ± 0.43
	Busan/Ulsan	14 (7.9)	2.86 ± 0.34
	Gangwon	5 (2.8)	2.75 ± 0.27
Grades	3.0–3.4	43 (24.2)	2.78 ± 0.38	0.263(.769)
	3.5–3.9	84 (47.2)	2.83 ± 0.40
	≥4.0	51 (28.7)	2.81 ± 0.30
Satisfaction with nursing major	Satisfied (a)	104 (58.4)	2.91 ± 0.35	18.247 (<.001)a > b > c
	Not sure (b)	63 (35.4)	2.74 ± 0.32
	Dissatisfied (c)	11 (6.2)	2.31 ± 0.27
Satisfaction with lectures	Satisfied (a)	83 (46.6)	2.95 ± 0.37	11.919 (<.001)a > b, c
	Not sure (b)	83 (46.6)	2.70 ± 0.31
	Dissatisfied (c)	12 (6.7)	2.63 ± 0.39
Satisfaction with simulation practice	Satisfied (a)	85 (47.8)	2.96 ± 0.39	16.121 (<.001)a > b, c
	Not sure (b)	74 (41.6)	2.71 ± 0.28
	Dissatisfied (c)	19 (10.7)	2.55 ± 0.28
Satisfaction with overall university education	Satisfied (a)	77 (43.3)	2.95 ± 0.41	10.738 (<.001)a > b, c
	Not sure (b)	86 (48.3)	2.73 ± 0.30
	Dissatisfied (c)	15 (8.4)	2.62 ± 0.28

No differences were found in practice readiness based on gender, age, university region, or grade point average. However, significant differences were observed depending on satisfaction with nursing major (F = 18.247, p < .001), lectures (F = 11.919, p < .001), simulation practice (F = 16.121, p < .001), and overall university education (F = 10.738, p < .001) (Table 1).

Correlations Between Social Intelligence, Clinical Practical Educational Environment, and Practice Readiness

The mean scores for social intelligence and clinical practical educational environment were 5.06 ± 0.71 points and 3.55 ± 0.74, respectively. The overall mean score for practice readiness was 2.81 ± 0.37 points. Among the subfactors, professional identity scored the highest (2.96 ± 0.47), whereas trials and tribulations scored the lowest (2.61 ± 0.45; Table 2). Among the main variables, social intelligence (r = .61, p < .001) and clinical practical education environment (r = .53, p < .001) showed positive correlations with practice readiness.

Table 2.

Descriptive Statistics and Correlations Between the Main Variables (N = 178).

	Min	Max	M	SD	SI	CPEE
	Min	Max	M	SD	r
Social intelligence	3.05	7.00	5.06	0.71	1
Clinical practical educational environment	1.42	5.00	3.55	0.74	.306***	1
Practice readiness	1.80	4.00	2.81	0.37	.612***	.531***
Clinical problem solving	1.57	4.00	2.84	0.44	.540***	.429***
Learning techniques	1.00	4.00	2.85	0.63	.237**	.432***
Professional identity	1.40	4.00	2.96	0.47	.548***	.466***
Trials and tribulations	1.40	4.00	2.61	0.45	.430***	.299***

Note. M = mean; SD = standard deviation; SI = social intelligence, CPEE = clinical practical educational environment.

**p < .01, ***p < .001.

Factors Related to Practice Readiness

Among the general characteristics, those associated with significant differences in practice readiness included satisfaction with major, lectures, simulation practice, and overall university education, and were added to model 1 as covariates. Thereafter, the independent variables of social intelligence and clinical practical educational environment were added to model 2 for a hierarchical regression analysis.

Before the regression analysis, the researchers verified that all necessary assumptions were satisfied. When the researchers examined the autocorrelation of the dependent variable using the Durbin–Watson index, the result was 2.079, indicating that the variable was independent with no autocorrelation and that our data were suitable for regression analysis. To analyze the fit of the regression model, the researchers assessed the normality and homogeneity of variance of the residuals. A Kolmogorov–Smirnov test on the normalized residuals verified a normal distribution (p = .200). Moreover, visual inspection of the scatter plot of the normalized residuals showed a random distribution centered on 0 and within ±3, with no regularity or trends, confirming homogeneity of the variance of the residuals. When the researchers tested the collinearity of the independent variables, the variance inflation factor was 1.14–1.98, which was smaller than 10, indicating no collinearity. The fit values for regression models 1 and 2 were significant (F = 4.761, p < .001; F = 34.939, p < .001).

In the final model, satisfaction with simulation practice (β = .20, p = .001), satisfaction with overall university education (β = .15, p = .041), social intelligence (β = .49, p < .001), and clinical practical educational environment (β = .28, p < .001) were identified as factors associated with practice readiness (Table 3). The final regression model explained 53.5% of the variance in practice readiness, representing a large effect size (Cohen, 1988). The standardized coefficients indicate that social intelligence had a large effect (β = .49), while clinical practical educational environment had a medium effect (β = .28) on practice readiness.

Table 3.

Factors Related to Practice Readiness.

	Model 1					Model 2
Characteristic	B	SE	β	t	p	B	SE	β	t	p
(Constant)	1.87	0.13		14.58		0.68	0.15		4.48
Satisfaction with major	0.16	0.05	.26	3.23	.001	0.07	0.04	.11	1.74	.084
Satisfaction with lecture	0.10	0.05	.16	1.97	.05	0.05	0.04	.09	1.37	.172
Satisfaction with simulation practice	0.15	0.04	.28	3.75	<.001	0.11	0.03	.20	3.41	.001
Satisfaction with overall university education	0.02	0.05	.03	0.28	.779	0.09	0.04	.15	2.06	.041
Social intelligence						0.25	0.03	.49	8.91	<.001
Clinical practical educational environment						0.14	0.03	.28	4.17	<.001
	R² = .254, adjusted R² = .237 F = 4.761, p < .001					R² = .551, adjusted R² = .535 F = 34.939, p < .001

Discussion

This study sought to determine nursing students’ practice readiness and the factors related to it. This study found moderate-level subjective practice readiness, similar to that reported in a previous Korean study using the same instruments (Lee et al., 2023). Among subfactors, nursing college graduates exhibited high confidence in professional identity and learning technique and low confidence in trials and challenging circumstances, including nursing records, setting priorities, and ethical problems. These findings align with those of previous studies (Casey et al., 2011; Rusch et al., 2019; Schmitt & Lancaster, 2019) reporting that nursing students scored high on professional traits while struggling with setting priorities, managing ethical problems, and providing deathbed nursing. This consistent pattern across studies may be explained by the inherent challenges nursing students face when transitioning from theoretical knowledge to complex clinical decision-making, particularly in ethically sensitive situations and prioritization under pressure. Cultural factors in Korea, such as hierarchical educational structures and limited exposure to real-world ethical dilemmas during training, might also contribute to lower confidence in these areas (Kim & Oh, 2016; Wessel et al., 2008). Furthermore, the rapid expansion of nursing programs may have diluted clinical practice opportunities, exacerbating difficulties in mastering these competencies (Song & Kim, 2013). Understanding these underlying factors may inform the development of educational interventions that move beyond knowledge acquisition to emphasize critical thinking, ethical reasoning, and practical prioritization skills in authentic clinical contexts, though further research is needed to establish causal relationships.

The present study showed that, nursing college graduates’ satisfaction with their simulation practice was correlated with practice readiness. Simulation education is an innovative method wherein various clinical settings are recreated within a safe and controlled learning environment, allowing students to perform nursing care directly and to correct erroneous practices through immediate feedback (Tong et al., 2022). Simulation practice allows students to improve their core nursing competencies, such as critical thinking and clinical performance in diverse and complex emergencies, while improving their communication and team performance skills (Tong et al., 2022), which may explain the correlation between high-quality simulation practice and practice readiness. This relationship suggests that positive experiential learning environments may be associated with both technical skill development and higher levels of student confidence and adaptability, which are essential for real-world clinical challenges. Differences in simulation resources and instructor expertise across institutions may also influence the effectiveness of such training (Lee et al., 2018; Tong et al., 2022). However, despite the increasing need for simulation practice based on its proven effectiveness, limitations exist in developing effective simulation practice environments in real education settings, including difficulties in developing valid scenarios that vividly recreate clinical settings and a shortage of educators capable of running such scenarios (Lee et al., 2018).

In this study, nursing college students’ social intelligence showed the strongest correlation with practice readiness, which could be attributed to the various roles of social intelligence. For example, people with high social intelligence show stronger social bonds with classmates, which leads to more active interactions and can improve essential abilities for nursing work, such as problem-solving, communication, teamwork, and leadership skills (Bulmer Smith et al., 2009; Han & Johnson, 2012; Rahim et al., 2018). From another perspective, social intelligence is a psychological protective factor that can reduce the negative effects of stress from interpersonal relationships (Goleman & Boyatzis, 2008). Therefore, these findings suggest that social intelligence may be associated with better regulation of academic stress and stress from various social relationships, such as those with peers, instructors, and patients (or caregivers), potentially contributing to improved academic resilience and higher levels of practice readiness (Alam et al., 2021; Hwang & Kim, 2023; Miri et al., 2013). This highlights the multifaceted role of social intelligence, not only as a skill set but also as a psychological resource that buffers stress and fosters engagement. Variations in cultural norms around communication and hierarchy may further shape how social intelligence develops and impacts readiness in different contexts. Despite the considerable stress caused by excessive workloads and clinical practice in nursing education, in addition to forming interpersonal relationships being an essential ability for nursing students and nurses, there have been limited efforts to improve social intelligence within this discipline.

This study also identified a notable correlation between clinical practice educational environment and practice readiness. This aligns with previous studies, wherein the clinical practice educational environment influenced not only educational outcomes but also satisfaction and confidence (Flott & Linden, 2016; Henderson et al., 2010; Levett-Jones & Lathlean, 2009). Further, students experience decreased anxiety during clinical practice if they perceive the educational environment positively, which can also increase practice readiness (Lee et al., 2023). Conversely, a poor clinical practice environment causes anxiety, decreases confidence, and potentially affects occupational satisfaction, ultimately leading to difficulties in maintaining nurse staffing and contributing to global nurse shortages (Flott & Linden, 2016; Mansutti et al., 2017). Thus, it is necessary to create a clinical practice environment, considering the atmosphere, student–staff relationships, nurse managers’ attitudes and guidance, and students’ relationships with patients. Positive clinical environments likely promote learning by reducing anxiety and encouraging active participation, whereas negative settings may hinder skill acquisition and motivation. Institutional constraints such as staffing shortages and limited mentorship can exacerbate these challenges, especially in rapidly expanding educational systems (Song & Kim, 2013). However, in Korea, a recent rapid increase in nursing colleges has resulted in difficulties in securing high-quality clinical practice settings and qualified instructors to nurture students effectively (Song & Kim, 2013). Further, students experience stress from clinical practice owing to a theory–practice gap, unfamiliar practice environments, ambiguity in their role, nurses’ uninterested or nonpedagogical attitudes, and their own undeveloped clinical ability (Kovner et al., 2014). Given the improved awareness of human rights and safety, limited access to medical records and observation-based practice further exacerbate problems with providing high-quality learning experiences for nursing students.

Moreover, positive perceptions of simulation practice and clinical practical educational environments are correlated with practice readiness, indicating a need to improve the quality of simulation practice and clinical practical education. One important related factor is academic–clinical partnerships (Bvumbwe, 2016). Universities and institutions can form partnerships with the shared goal of nursing education, reduce the theory–practice gap through resource-sharing and collaboration while implementing evidence-based nursing practice to improve nursing students’ nursing competencies, boost their self-confidence, and increase their practice readiness (Bvumbwe, 2016; Pedregosa et al., 2020).

Strengths and Limitations

This study has several limitations. First, the cross-sectional design precludes causal inferences between the variables. A longitudinal study design is needed to obtain a more in-depth understanding of the mechanisms by which the variables affect one another. Second, the use of convenience sampling likely resulted in some bias in participants’ gender and region; thus, the results must be interpreted cautiously. Convenience sampling introduces selection bias, which limits the representativeness of the sample and reduces the generalizability of the findings. In particular, since 88.2% of participants were female, reflecting the typical gender distribution in Korean nursing programs, the findings may not be fully generalizable to male nursing students or more gender-diverse populations. This gender imbalance may limit the understanding of how practice readiness factors, particularly social intelligence and interpersonal skills, manifest differently across gender groups. Third, our literature review predominantly draws from East Asian research contexts, which may limit the cross-cultural applicability of our findings. Future research should incorporate broader international perspectives to enhance generalizability across diverse educational and cultural settings. Finally, since a self-report questionnaire was used to evaluate nursing students’ practice readiness and social intelligence, several biases may have influenced the findings. Self-report measures are susceptible to social desirability bias, particularly in Korean cultural contexts where modesty and professional competence are highly valued, potentially leading to an overestimation of both social intelligence and practice readiness. Additionally, students’ self-perceptions may not accurately reflect their actual clinical performance, and this measurement bias could have artificially inflated the observed correlations between variables. The cross-sectional nature of this study, combined with self-report bias, limits our ability to determine whether the relationships we observed reflect genuine associations or are partially artifacts of common method variance. In the future, in addition to self-evaluation methods, teaching staff or clinical educators should provide objective evaluations of students’ practice readiness, considering knowledge, attitude, and skills. Nevertheless, this study is valuable insofar as it evaluated the current outcomes of university education in South Korea by investigating nursing college graduates’ practice readiness and providing basic data to help identify areas requiring additional educational support.

Implications for Practice

The findings of this study have several important implications for nursing education and practice. First, nursing programs should integrate repeated practice opportunities involving complex virtual scenarios, electronic nursing records systems, and real case analyses to address graduates’ weaknesses in documentation, prioritization, and ethical decision-making. A competency-based curriculum that systematically trains students in professional competencies from early stages of their studies is essential for improving practice readiness.

Second, given the strong correlation between social intelligence and practice readiness, nursing curricula should incorporate programs that are specifically designed to enhance students’ interpersonal skills, communication abilities, and stress management capabilities. These programs should be integrated throughout the curriculum rather than offered as standalone courses, allowing students to develop these competencies progressively alongside their technical skills.

Third, educational institutions need enhanced support to develop effective simulation environments, including valid scenarios that accurately reflect clinical settings, strategic practical courses, and skilled educators capable of implementing high-quality simulation training. This requires investment in both technological infrastructure and faculty development to ensure simulation experiences meaningfully contribute to practice readiness.

Fourth, measures are needed to create positive clinical practice environments that consider atmosphere, student–staff relationships, mentorship quality, and patient interactions. These measures include addressing staffing shortages, providing adequate mentorship, and ensuring access to meaningful learning experiences that bridge the theory–practice gap.

Finally, universities and healthcare institutions should establish stronger partnerships to reduce theory–practice gaps, share resources, implement evidence-based practices, and ultimately improve students’ competencies and confidence levels. These partnerships should include collaborative curriculum development, shared clinical placements, and joint evaluation of student outcomes to ensure graduates are adequately prepared for professional practice.

Conclusion

This study reveals that enhancing practice readiness among nursing graduates requires a dual approach targeting both individual competencies and systemic educational reforms. The strong association between social intelligence and practice readiness suggests that nursing education must move beyond technical skill development to cultivate interpersonal and adaptive competencies that enable successful professional role transition. Specifically, nursing curricula should be reformed to integrate simulation-based learning across courses, foster social intelligence and communication skills through dedicated modules, and enhance clinical education environments by strengthening partnerships with clinical sites and providing structured, competency-based learning opportunities. These educational reforms can bridge the gap between theory and practice, ultimately supporting the smoother transition for graduates into professional nursing roles. We propose that further studies explore educational needs to develop user-centric education and improve practice readiness among nursing students and clinical workers. Furthermore, it is recommended that future longitudinal studies explore the long-term impact of social intelligence and the clinical learning environment on nursing students’ readiness for practice and job adjustment.

Footnotes

Acknowledgments

We would like to thank all the students who participated in and supported this study. We would also like to appreciate Editage () for English-language editing.

ORCID iDs

Kim Jihye

Lee Kyungmi

Ethical Considerations

This study was approved by the institutional review board (IRB) of Woosuk University (IRB No. WS-2023-09) on May 2023. Adhering to ethical principles, participants received an explanation sheet outlining the study's purpose, methods, the voluntary nature of their participation, and their right to withdraw consent at any time without facing any penalties. Participants were required to complete an online consent form indicating their voluntary participation. All procedures were performed according to the Declaration of Helsinki.

Consent to Participate

This study was approved by the Institutional Review Board of Woosuk University (Approval number: WS-2023-09). All participants were informed about the study's purpose and procedures, and provided written informed consent for participation. Participants were notified that they could withdraw from the study at any time without any consequences.

Consent for Publication

All participants provided written informed consent for the publication of research findings, including anonymized data. Participants were informed that their personal information would be protected through anonymization, and that the research results would be used solely for academic purposes.

Authors’ Contributions

Conceptualization and methodology: K. J. and L. K.; data curation and analysis: K. J.; investigation: K. J. and L. K.; validation: L. K.; visualization: K. J. and L. K.; writing: K. J. and L. K. Both authors read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Science and ICT, South Korea (grant number RS-2022-00165947).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

Data are available by contacting the corresponding author.

References

Alam

Yang

Bhutto

M. Y.

Akhtar

(2021). The influence of e-learning and emotional intelligence on psychological intentions: Study of stranded Pakistani students. Frontiers in Psychology, 12, 715700. https://doi.org/10.3389/fpsyg.2021.715700

Allen

M. S.

Iliescu

Greiff

(2022). Single item measures in psychological science: A call to action. European Journal of Psychological Assessment, 38(1), 1–5. https://doi.org/10.1027/1015-5759/a000699

Arkan

Ordin

Yılmaz

(2018). Undergraduate nursing students’ experience related to their clinical learning environment and factors affecting to their clinical learning process. Nurse Education in Practice, 29, 127–132. https://doi.org/10.1016/j.nepr.2017.12.005

Benner

(1984). From novice to expert: Excellence and power in clinical nursing practice. Addison-Wesley.

Bulmer Smith

K. B.

Profetto-McGrath

Cummings

G. G.

(2009). Emotional intelligence and nursing: An integrative literature review. International Journal of Nursing Studies, 46(12), 1624–1636. https://doi.org/10.1016/j.ijnurstu.2009.05.024

Bvumbwe

(2016). Enhancing nursing education via academic–clinical partnership: An integrative review. International Journal of Nursing Sciences, 3(3), 314–322. https://doi.org/10.1016/j.ijnss.2016.07.002

Cao

Gong

(2021). Effects of resilience, social support, and work environment on turnover intention in newly graduated nurses: The mediating role of transition shock. Journal of Nursing Management, 29(8), 2585–2593. https://doi.org/10.1111/jonm.13418

Casey

Fink

Jaynes

Campbell

Cook

Wilson

(2011). Readiness for practice: The senior practicum experience. Journal of Nursing Education, 50(11), 646–652. https://doi.org/10.3928/01484834-20110817-03

Chun

Park

Y. S.

Oak

J. W.

(2021). Validation of the Korean version of the undergraduate clinical education environment measure. Korean Medical Education Review, 23(1), 37–45. https://doi.org/10.17496/kmer.2021.23.1.37

10.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

11.

Collard

S. S.

Scammell

Tee

(2020). Closing the gap on nurse retention: A scoping review of implications for undergraduate education. Nurse Education Today, 84, 104253. https://doi.org/10.1016/j.nedt.2019.104253

12.

Duchscher

J. E. B.

(2009). Transition shock: The initial stage of role adaptation for newly graduated registered nurses. Journal of Advanced Nursing, 65(5), 1103–1113. https://doi.org/10.1111/j.1365-2648.2008.04898.x

13.

Flott

E. A.

Linden

(2016). The clinical learning environment in nursing education: A concept analysis. Journal of Advanced Nursing, 72(3), 501–513. https://doi.org/10.1111/jan.12861

14.

Goleman

(2006). Social intelligence: The new science of human relationships. Bantam Books.

15.

Goleman

Boyatzis

(2008). Social intelligence and the biology of leadership. Harvard Business Review, 86(9), 74–81.

16.

Han

Johnson

S. D.

(2012). Relationship between students’ emotional intelligence, social bond, and interactions in online learning. Educational Technology and Society, 15(1), 78–89.

17.

Henderson

Twentyman

Eaton

Creedy

Stapleton

Lloyd

(2010). Creating supportive clinical learning environments: An intervention study. Journal of Clinical Nursing, 19(1–2), 177–182. https://doi.org/10.1111/j.1365-2702.2009.02841.x

18.

Hoerger

(2010). Participant dropout as a function of survey length in internet-mediated university studies: Implications for study design and voluntary participation in psychological research. Cyberpsychology, Behavior and Social Networking, 13(6), 697–700. https://doi.org/10.1089/cyber.2009.0445

19.

Hwang

E. H.

Kim

K. H.

(2023). Relationship between optimism, emotional intelligence, and academic resilience of nursing students: The mediating effect of self-directed learning competency. Frontiers in Public Health, 11, 1182689. https://doi.org/10.3389/fpubh.2023.1182689

20.

Kim

E. Y.

Yeo

J. H.

(2021). Transition shock and job satisfaction changes among newly graduated nurses in their first year of work: A prospective longitudinal study. Journal of Nursing Management, 29(3), 451–458. https://doi.org/10.1111/jonm.13164

21.

Kim

Shin

(2022). Development of the nursing practice readiness scale for new graduate nurses: A methodological study. Nurse Education in Practice, 59, 103298. https://doi.org/10.1016/j.nepr.2022.103298

22.

Kim

(2016). Assimilating to hierarchical culture: A grounded theory study on communication among clinical nurses. PLoS One, 11(6), e0156305. https://doi.org/10.1371/journal.pone.0156305

23.

Kovner

C. T.

Brewer

C. S.

Fatehi

Jun

(2014). What does nurse turnover rate mean and what is the rate? Policy, Politics and Nursing Practice, 15(3–4), 64–71. https://doi.org/10.1177/1527154414547953

24.

Kramer

(1974). Reality shock: Why nurses leave nursing. Mosby.

25.

Lee

J. Y.

Lee

S. H.

Kim

J. H.

(2018). A review of the curriculum development process of simulation-based educational intervention studies in Korea. Nurse Education Today, 64, 42–48. https://doi.org/10.1016/j.nedt.2018.01.029

26.

Lee

S. H.

Kim

J. H.

Jung

Kang

S. J.

(2019). Educational needs for new graduate nurses in Korea. Nurse Education in Practice, 34, 167–172. https://doi.org/10.1016/j.nepr.2018.11.021

27.

Lee

Kim

(2024). The mediating effect of transition shock on the relationship between readiness for practice and turnover intention of new graduate nurses in South Korea: A longitudinal study. Nurse Education Today, 143, 106394. https://doi.org/10.1016/j.nedt.2024.106394

28.

Lee

S. J.

Yoon

Y. S.

Yoon

Lee

(2023). Personal factors and clinical learning environment as predictors of nursing students’ readiness for practice: A structural equation modeling analysis. Asian Nursing Research, 17(1), 44–52. https://doi.org/10.1016/j.anr.2023.01.003

29.

Lee

T. W.

Yoon

Y. S.

(2022). The validity and reliability of the Korean version of readiness for practice survey for nursing students. Journal of Korean Academy of Nursing, 52(6), 564–581. https://doi.org/10.4040/jkan.22032

30.

Leong

Y. M. J.

Crossman

(2015). New nurse transition: Success through aligning multiple identities. Journal of Health Organization and Management, 29(7), 1098–1114. https://doi.org/10.1108/JHOM-02-2014-0038

31.

Levett-Jones

Lathlean

(2009). The ascent to competence conceptual framework: An outcome of a study of belongingness. Journal of Clinical Nursing, 18(20), 2870–2879. https://doi.org/10.1111/j.1365-2702.2008.02593.x

32.

Mansutti

Saiani

Grassetti

Palese

(2017). Instruments evaluating the quality of the clinical learning environment in nursing education: A systematic review of psychometric properties. International Journal of Nursing Studies, 68, 60–72. https://doi.org/10.1016/j.ijnurstu.2017.01.001

33.

Marlowe

H. A.

(1986). Social intelligence: Evidence for multidimensionality and construct independence. Journal of Educational Psychology, 78(1), 52–58. https://doi.org/10.1037/0022-0663.78.1.52

34.

Miri

M. R.

Kermani

Khoshbakht

Moodi

(2013). The relationship between emotional intelligence and academic stress in students of medical sciences. Journal of Education and Health Promotion, 2(1), 40. https://doi.org/10.4103/2277-9531.115836

35.

Mirza

Manankil-Rankin

Prentice

Hagerman

L. A.

Draenos

(2019). Practice readiness of new nursing graduates: A concept analysis. Nurse Education in Practice, 37, 68–74. https://doi.org/10.1016/j.nepr.2019.04.009

36.

Moore

Palerino

Pawloski

Desmond

Erickson

Salkic-Mehkic

(2023). The effects of clinical immersion on readiness for nursing practice. Journal of Nursing Education, 62(1), 47–50. https://doi.org/10.3928/01484834-20221109-08

37.

Nishioka

V. M.

Coe

M. T.

Hanita

Moscato

S. R.

(2014). Dedicated education unit: Nurse perspectives on their clinical teaching role. Nursing Education Perspectives, 35(5), 294–300. https://doi.org/10.5480/14-1381

38.

Pedregosa

Fabrellas

Risco

Pereira

Dmoch-Gajzlerska

Şenuzun

Martin

Zabalegui

(2020). Effective academic–practice partnership models in nursing students’ clinical placement: A systematic literature review. Nurse Education Today, 95, 104582. https://doi.org/10.1016/j.nedt.2020.104582

39.

Rahim

Civelek

Liang

F. H.

(2018). A process model of social intelligence and problem-solving style for conflict management. International Journal of Conflict Management, 29(4), 487–499. https://doi.org/10.1108/IJCMA-06-2017-0055

40.

Rusch

Manz

Hercinger

Oertwich

McCafferty

(2019). Nurse preceptor perceptions of nursing student progress toward readiness for practice. Nurse Educator, 44(1), 34–37. https://doi.org/10.1097/NNE.0000000000000546

41.

Schmitt

C. A.

Lancaster

R. J.

(2019). Readiness to practice in generation Z nursing students. Journal of Nursing Education, 58(10), 604–606. https://doi.org/10.3928/01484834-20190923-09

42.

Silvera

D. H.

Martinussen

Dahl

T. I.

(2001). The Tromsø Social Intelligence Scale, a self-report measure of social intelligence. Scandinavian Journal of Psychology, 42(4), 313–319. https://doi.org/10.1111/1467-9450.00242

43.

Song

J. H.

Kim

M. W.

(2013). Study on clinical education for nursing in hospitals in Korea. Journal of Korean Academic Society of Nursing Education, 19(2), 251–264. https://doi.org/10.5977/jkasne.2013.19.2.251

44.

Strand

Sjöborg

Stalmeijer

Wichmann-Hansen

Jakobsson

Edgren

(2013). Development and psychometric evaluation of the undergraduate clinical education environment measure (UCEEM). Medical Teacher, 35(12), 1014–1026. https://doi.org/10.3109/0142159X.2013.835389

45.

Tong

L. K.

Y. Y.

M. L.

Wang

S. C.

W. I.

(2022). High-fidelity simulation duration and learning outcomes among undergraduate nursing students: A systematic review and meta-analysis. Nurse Education Today, 116, 105435. https://doi.org/10.1016/j.nedt.2022.105435

46.

Tseng

M. Y.

Hwang

S. L.

(2021). “SURVIVAL” intervention program effects on student nurses’ transition to staff nurses: A quasi-experimental study. Nurse Education in Practice, 56, 103184. https://doi.org/10.1016/j.nepr.2021.103184

47.

van Rooyen

D. R. M.

Jordan

P. J.

Ham-Baloyi

Caka

(2018). A comprehensive literature review of guidelines facilitating transition of newly graduated nurses to professional nurses. Nurse Education in Practice, 30, 35–41. https://doi.org/10.1016/j.nepr.2018.02.010

48.

Wessel

Larin

Benson

Brown

Ploeg

Williams

(2008). Emotional–social intelligence in health science students and its relation to leadership, caring and moral judgment. Internet Journal of Allied Health Sciences and Practice, 6(1), 5. https://doi.org/10.46743/1540-580X/2008.1180

49.

World Health Organization (WHO) . (2020). State of the world’s nursing 2020: Investing in education, jobs and leadership. https://apps.who.int/iris/handle/10665/331677