Introduction
Nursing is a profession centered on aiding and supporting others, requiring nurses to effectively manage interpersonal relationships in problem-solving, decision-making, and professional conduct [1]. Accordingly, in addition to acquiring knowledge and skills, nursing students must develop the ability to navigate interactions with diverse individuals and situations [2].
However, interpersonal sensitivity, as a personality trait, has been shown to exhibit susceptibility and variability in response to external factors (e.g., interactions with diverse individuals and situations) [3]. In addition, individuals with heightened interpersonal sensitivity in the workplace often exhibit a more pessimistic outlook compared to their counterparts, which can lead to conflicts with others and challenges in effectively managing interpersonal relationships [4]. Moreover, individuals with higher interpersonal sensitivity are attuned to perceived or actual criticism and rejection, prompting them to adjust their behavior to align with the expectations of those around them, further contributing to their reluctance to interact with others [4]. Consequently, these individuals are often avoided by their peers, resulting in increased feelings of isolation. This state of isolation can be characterized as loneliness, defined as the subjective feeling of a lack of social contact [5].
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One study has established an association between interpersonal sensitivity and loneliness among individuals aged 17 to 26 years [6]. This robust association still exists in other special studies, such as those involving Chinese gay men [7]. Another study conducted among Chinese college students has shown a positive correlation between interpersonal sensitivity and negative emotions [8], as well as an association with individuals’ mobile phone addictions [9], which are in turn associated with feelings of loneliness [10]. Nonetheless, there is a lack of investigations examining the central symptoms and bridge symptoms of interpersonal sensitivity and loneliness.
Central symptoms and bridge symptoms are essential for providing precise descriptions of salient symptoms. In contrast to the conventional practice of relying on total scale scores, central symptoms within the network highlight the significance of a node [11]. For example, a study has demonstrated that central symptoms are essential for identifying key symptoms in the association between depression and anxiety, as well as for effectively developing targeted intervention strategies [12]. Moreover, bridge symptoms within the network are pivotal in both perpetuating and fostering comorbidities, offering valuable insights for preventing and managing these concurrent conditions [13]. Currently, central symptoms and bridge symptoms are revealed through network analysis. For instance, based on network analysis, a study has identified the core role of loneliness in the network structure of paranoia dimensions (i.e., interpersonal sensitivity, mistrust, and ideas of persecution) in the general population, proposing that addressing loneliness may serve as a beneficial focal point for clinical intervention [11].
To date, there is a dearth of research investigating the association between interpersonal sensitivity and loneliness symptoms utilizing a network model among college nursing students. This knowledge gap motivated the current study, which aimed to explore the associations between interpersonal sensitivity and loneliness symptoms in Chinese college nursing students through a network analysis approach. Ultimately, these findings can inform public health policies and interventions designed to enhance the well-being of individuals during their college life.
Method
Study design and participants
The present study was carried out at Jitang College of North China University of Science and Technology in Tangshan, Hebei, China. A single-center cluster sampling approach was utilized for this study. Data were collected through the Questionnaire Star platform from November to December 2022, a widely used online survey tool in China. The sample size was determined using the formula applicable to cross-sectional studies [n = (Z2α/2pq) / δ2] [14]. In this formula: (1) n represents the sample size; (2) p indicates the prevalence rates of interpersonal sensitivity and loneliness based on previous studies; (3) q is defined as (1- p); (4) Zα/2 is set at 1.96, corresponding to an α level of 0.05 for a two-tailed test; and (5) δ reflects the permissible error, calculated as 0.1p. Previous research has shown that the prevalence of common mental health issues, including interpersonal sensitivity, among college students in China ranges from 10 to 30% [15], while the incidence of loneliness in industrialized countries is reported to be 33.3% [16]. Consequently, the study necessitated a minimum of 780 participants to reach the desired sample size. In addition, students were informed about the study’s objectives and associated protocols. Informed consent was secured from all participants, and data anonymization procedures were strictly adhered to. The inclusion criteria comprised: (1) undergraduate students enrolled at the university; (2) willingness to take part in the study; and (3) no concurrent participation in other similar studies. The exclusion criteria comprised participants who did not complete the questionnaires or completed them in less than 60 s. Ultimately, a total of 864 nursing students participated in this study.
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This study was conducted following the principles outlined in the Declaration of Helsinki and the Measures for Ethical Review of Biomedical Research Involving Human Beings. Ethical approval was granted by the Ethics Review Committee of Jitang College, North China University of Science and Technology (JTXY-2022-002).
Measures
Demographic data include age, gender, urban-rural distribution, academic grade, depression symptoms, and anxiety symptoms.
Interpersonal sensitivity
The Chinese Version of the Short Form of the Interpersonal Sensitivity Measure (IPSM-CS) was used to investigate participants’ interpersonal sensitivity across five dimensions: interpersonal awareness (3 items), need for approval (3 items), separation anxiety (3 items), timidity (3 items), and fragile inner-self (3 items) [17]. Each items using a 5-point scale of 1 to 5 (1= “very unlike me” to 5= “very like me”), with higher scores indicating higher levels of interpersonal sensitivity. In the present study, the Cronbach’s alpha coefficient for the IPSM-CS was 0.897.
Loneliness
The Three-Item Loneliness Scale (T-ILS) was used to investigate participants’ loneliness symptoms across three dimensions: relational loneliness (1 item), social loneliness (1 item), and intimate loneliness (1 item) [18]. Each items using a 3-point scale of 1 to 3 (1= “hardly ever” to 3= “often”), with higher scores indicating higher levels of loneliness. In the present study, the Cronbach’s alpha coefficient for the T-ILS was 0.866.
Depression symptoms
The Patient Health Questionnaire-9 (PHQ-9) was used to investigate participants’ depressive symptoms [19]. It comprises 9 items, each item using a 4-point scale of 0 to 3 (0= “never” to 3= “nearly every day”). The aggregate score for the PHQ-9 varies from 0 to 27, where a higher score corresponds to a greater depression level. Following previous research [20], depression symptoms are defined operationally as PHQ-9 scores of 10 or above. In the present study, the Cronbach’s alpha coefficient for the PHQ-9 was 0.930.
Anxiety symptoms
The Generalized Anxiety Disorder-7 (GAD-7) was used to investigate participants’ anxiety symptoms [21]. It comprises 7 items, each item using a 4-point scale of 0 to 3 (0= “never” to 3= “nearly every day”). The aggregate score for the GAD varies from 0 to 21, where a higher score corresponds to a greater anxiety level. Following previous research [20], anxiety symptoms are defined operationally as GAD-7 scores of 10 or above. In the present study, the Cronbach’s alpha coefficient for the GAD-7 was 0.952.
Statistical analyses
First, the normality of continuous variables was assessed using the Kolmogorov-Smirnov test and Q-Q plots. Since all continuous variables were found to have a normal distribution, they were reported as mean and standard deviation (SD). Categorical data were presented as counts and percentages. Then, the network analysis encompassed three domains: network estimation, network stability, and network comparisons.
Network estimation
In the context of network analysis, each item was represented as a node, while the connection between two nodes was considered an edge. The relationship between each pair of nodes was determined through partial correlation analysis, with adjustments made to account for potential confounding effects from all other nodes. The least absolute shrinkage and selection operator was employed to reduce all connections within the network and eliminate small correlations by setting them to zero [22]. This process allows for the preservation of nodes with only essential edges in the network. The Extended Bayesian Information Criteria was utilized to select the appropriate tuning parameter, aiming to create a sparser network that is easier to interpret [23]. Then, we carried out two independent network structures (network structure of interpersonal sensitivity and loneliness symptoms in college nursing students; network structure of interpersonal sensitivity and loneliness bridge symptoms in college nursing students) using the R packages bootnet (Version 1.4.3) and qgraph (Version 1.6.9) for network estimation and visualization [24]. In the network layout, the thickness of edges reflects the strength of the associations, with blue edges representing positive associations and red edges representing negative associations.
Centrality indices were computed to evaluate the importance of each node within the network, utilizing the centralityPlot function from the R package qgraph (Version 1.6.9) [24]. The network was typically characterized using various centrality indices, including strength, betweenness, and closeness [25]. Prior study has suggested that the estimates of closeness and betweenness might lack reliability [26]. Therefore, this study utilized the centrality index of strength, which is the most commonly used measure calculated by aggregating the weights of the edges that link the node to other nodes. In this study, predictability was evaluated as an indicator of the interconnectedness and impact of a node on its adjacent nodes [27]. In the network visualization, the size of the rings surrounding each node represents the predictability value, calculated using the predict function from the R package mgm (Version 1.2–11) [28]. The bridge centrality index for bridge strength was also analyzed using the bridge function from the R package networktools (Version 1.2.3) [29].
Network stability
The stability of node strength and bridge strength was evaluated through a case-dropping bootstrap procedure. Such method involved systematically removing an increasing proportion of cases from the dataset and re-estimating the centrality indices. A network is deemed stable if a substantial portion of the sample can be removed without causing significant alterations in the indices [23]. Stability is measured by the Correlation Stability Coefficient (CS-C), which indicates the highest number of cases that can be removed from the sample while preserving a correlation of r = 0.7 between the centrality indices of the subsamples and the original sample [23]. Typically, a CS-C value above 0.25 is considered acceptable, with values preferably exceeding 0.5 [23]. A nonparametric bootstrap procedure was employed to evaluate the stability of edge weights using 95% confidence intervals (95% CIs). The accuracy of edges was evaluated based on the width of the 95% CIs, where a narrower CI suggests a more reliable network [23]. Moreover, bootstrapped tests were employed based on 95% confidence intervals (CIs) to assess discrepancies in edge or node strength between two entities. Statistical disparities in edge or node strength were identified when zero did not fall within the CIs. The network stability analyses were conducted using the R package bootnet (Version 1.4.3) [23].
Network comparison
The Network Comparison Test (NCT) from the R package NetworkComparisonTest (Version 2.2.1) was employed to evaluate the three measures of invariance (i.e., network structure invariance, edge invariance, and global strength) [30]. The network structure represents the largest discrepancy in pairwise edges between the two study networks, edge invariance signifies the variance in individual edge weight between the two networks, and global strength denotes the cumulative sum of all edges in each network [30].To account for multiple comparisons at the level of individual edges between the two networks, a Holm-Bonferroni correction was employed. Based on previous studies, interpersonal sensitivity showed gender and urban-rural differences [31, 32], and patients with depression and anxiety had higher scores on the IPSM [33, 34]. We compared network structure invariance, edge invariance, and global strength between different subgroups (e.g., between males and females, between rural and urban areas, and between individuals with or without symptoms of anxiety and depression).
All statistical tests were conducted as two-tailed tests, with statistical significance set at P < 0.05, and the statistical analyses were carried out using R software version 4.3.0.
Results
Descriptive statistics
Of the 864 college nursing students participating in this network, the mean age was 20.16 (standard deviation (SD) = 1.42) years, with 684 (79.17%) being female and 462 (53.47%) being rural residents (Table 1). The mean scores for the IPSM-CS and T-ILS items ranged from 2.30 to 3.69 and from 1.72 to 1.79, respectively (Table 2). The response distributions for the IPSM-CS and T-ILS items are presented in Tables S1 and S2, respectively.
Table 1
Summary of participant characteristics
Variables | Value |
|---|---|
Age (years), mean (SD) | 20.16 (1.42) |
Gender, n (%) | |
Male | 180 (20.83) |
Female | 684 (79.17) |
Urban-rural distribution, n (%) | |
Rural | 462 (53.47) |
Urban | 402 (46.53) |
Academic grade, n (%) | |
First year | 192 (22.22) |
Second year | 174 (20.14) |
Third year | 242 (28.01) |
Fourth year | 256 (29.63) |
Depression symptoms, n (%) | |
No depression symptoms | 760 (87.96) |
Having depression symptoms | 104 (12.04) |
Anxiety symptoms, n (%) | |
No anxiety symptoms | 723 (83.68) |
Having anxiety symptoms | 141 (16.32) |
Table 2
Descriptive statistics of the IPSM-CS and T-ILS items
Item abbreviations | Item content | Item mean (SD) | Node Strengtha | Predictabilitya |
|---|---|---|---|---|
IPSM-CS1 | Feel insecure when say goodbye | 2.30 (0.99) | 1.111 | 0.635 |
IPSM-CS2 | They would not like me | 2.31 (0.98) | 1.522 | 0.655 |
IPSM-CS3 | Do not get angry | 2.57 (0.10) | 0.835 | 0.429 |
IPSM-CS4 | Worry being criticized | 2.84 (1.04) | 1.408 | 0.633 |
IPSM-CS5 | Worry losing something | 3.47 (1.14) | 1.096 | 0.513 |
IPSM-CS6 | Do something rather than offend | 3.00 (1.00) | 0.940 | 0.490 |
IPSM-CS7 | Appeal to someone | 2.92 (0.95) | 0.963 | 0.507 |
IPSM-CS8 | Feel anxious when say goodbye | 2.46 (0.99) | 1.314 | 0.634 |
IPSM-CS9 | Feel happy when someone compliments | 3.69 (0.95) | 1.661 | 0.709 |
IPSM-CS10 | Make others happy | 3.53 (0.91) | 1.324 | 0.663 |
IPSM-CS11 | Hard to get angry | 3.12 (0.94) | 0.748 | 0.296 |
IPSM-CS12 | Feel bad | 3.26 (0.96) | 0.985 | 0.536 |
IPSM-CS13 | Thinking less of me | 2.55 (0.90) | 1.166 | 0.549 |
IPSM-CS14 | Do not like people know | 2.59 (0.94) | 0.825 | 0.455 |
IPSM-CS15 | Worry others thinking | 3.08 (0.98) | 1.292 | 0.554 |
T-ILS1 | Relational loneliness | 1.79 (0.55) | 1.131 | 0.581 |
T-ILS2 | Social loneliness | 1.72 (0.58) | 1.178 | 0.611 |
T-ILS3 | Intimate loneliness | 1.75 (0.58) | 0.901 | 0.549 |
Network structure
The interpersonal sensitivity and loneliness symptoms network is depicted in Fig. 1, while the corresponding partial correlation matrix is provided in Table S3. The edge ‘Feel happy when someone compliments - Make others happy’ (IPSM-CS9-IPSM-CS10) shows the strongest association, followed by the edge ‘Feel insecure when say goodbye - Feel anxious when say goodbye’ (IPSM-CS1-IPSM-CS8) and ‘Relational loneliness - Social loneliness’ (T-ILS1- T-ILS2). In Table 2; Fig. 1, ‘Feel happy when someone compliments’ (IPSM-CS9) has the highest node strength in the interpersonal sensitivity and loneliness network among college nursing students, followed by ‘They would not like me’ (IPSM-CS2), ‘Worry being criticized’ (IPSM-CS4), and ‘Make others happy’ (IPSM-CS10). The item ‘Feel happy when someone compliments’ (IPSM-CS9) exhibited the highest predictability within the network (Table 1), with an average of 55.6% of variance potentially explained by the surrounding nodes of each item (Mpredictability = 0.556 ± 0.099). Regarding bridge symptoms, ‘Relational loneliness’ (T-ILS1) exhibited the highest bridge strength, followed by ‘Intimate loneliness’ (T-ILS3) and ‘Social loneliness’ (T-ILS2) (Fig. 2).
Fig. 1
Network structure of interpersonal sensitivity and loneliness symptoms in college nursing students. Figure 1A shows the visualization of the network structure; Fig. 1B shows the value of strength in order
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Fig. 2
Network structure of interpersonal sensitivity and loneliness showing bridge symptoms among the study nursing students. Figure 2A shows the visualization of the network structure of bridging symptoms; Fig. 2B shows the value of bridge strength in order
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Network stability
In Fig. 3, the case-dropping bootstrap procedure (n = 1000) demonstrated network stability, with CS-Cs of 0.75 for both node strength and bridge strength, suggesting that 75% of the samples could be eliminated without compromising stability. The findings from the nonparametric bootstrap procedure revealed that the majority of comparisons involving edge weights and node strength were found to be statistically significant (Figs. S1, S2). Furthermore, the narrow bootstrapped 95% CIs suggested that the edges were reliable (Fig. S3).
Fig. 3
The stability of strength and bridge strength using case-dropping bootstrap. The x-axis indicates the percentage of cases of the original sample included at each step. The y-axis indicates the average of correlations between the centrality indices from the original network and the centrality indices from the networks that were re-estimated after excluding increasing percentages of cases
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Network comparisons
As illustrated in Figs. S4-S7, a significant difference was observed in network structure invariance between rural and urban college nursing students (Rural: 8.799 vs. Urban: 8.922, M = 0.255, p = 0.030), between individuals with no anxiety symptoms and those with anxiety symptoms (No anxiety symptoms: 8.687 vs. Having anxiety symptoms: 7.919, M = 0.425, p = 0.010), and between individuals with no depression symptoms and those with depression symptoms (No depression symptoms: 8.658 vs. Having depression symptoms: 7.997, M = 0.364, p = 0.010). In the other subsample comparison, no significant differences were identified in network structure invariance (Males: 8.481 vs. Females: 8.607, M = 0.169, p = 0.733). Regarding the comparisons of network global strength and individual edge weights, no significant differences were found between the two networks across the four subsample comparisons.
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Discussion
To our knowledge, this study marks the first effort to outline the network of interpersonal sensitivity and loneliness among college nursing students. Positive correlations were noted between loneliness and most items of the IPSM-CS. Furthermore, the analysis indicated that ‘Feel happy when someone compliments’ (IPSM-CS9) exhibited the highest node strength within the network, with ‘They would not like me’ (IPSM-CS2) following closely behind. In addition, central symptoms (i.e., ‘Feel happy when someone compliments’ and ‘They would not like me’) and bridge symptoms (i.e., ‘Relational loneliness’, ‘Intimate loneliness’, and ‘Social loneliness’) were also recognized in this network. Finally, the network comparison test revealed a significant difference in network structure invariance between rural and urban college nursing students and between individuals with or without symptoms of anxiety and depression.
The degree of loneliness exhibited positive associations with most interpersonal sensitivity items. Previous studies have established an association between interpersonal sensitivity and loneliness among individuals aged 17 to 26 years or Chinese gay men [6, 7]. One possible explanation for this association is that nursing students with higher levels of interpersonal sensitivity, being more attuned to the attitudes and behaviors of others, may experience discomfort or distress in social interactions, which can lead to a tendency to avoid social situations and exacerbate feelings of loneliness [35]. Moreover, nursing students frequently bear emotional burdens arising from patients’ suffering, particularly during clinical placements and practical experiences [36]. Prolonged exposure to these emotional burdens can contribute to emotional stress, fatigue, and an increased sense of loneliness among these students [37]. These findings underscore the importance of providing nursing students with psychological support and strengthening their social support networks. Providing mental health support for nursing students can assist them in effectively coping with emotional stress, thereby reducing feelings of loneliness and promoting their psychological well-being and academic performance.
‘Feel happy when someone compliments’ (IPSM-CS9), which belonging to the category of need for approval exhibited the highest strength in the entire network, highlighting its significant role in the network of interpersonal sensitivity and loneliness among college nursing students. The node index of strength plays a crucial role in identifying influential symptoms that trigger and sustain psychopathological networks, serving as potential targets for interventions [11]. The reason why the need for approval is particularly important for nursing students may lie in the fact that nursing students often have to deal with heavy academic workload, and approval can provide emotional affirmation and encouragement, helping to enhance their confidence and motivation to cope with various challenges in their work [38]. In this study, we observed that ‘They would not like me’ (IPSM-CS2), which belongs to the category of fragile inner self also demonstrated high node strength values, suggesting its potential significant role in triggering and sustaining the network of interpersonal sensitivity and loneliness among college nursing students. A potential explanation for this phenomenon is that a fragile inner self typically indicates that nursing students respond with heightened sensitivity and intensity to emotions, rendering them more vulnerable to external circumstances and the actions of others, subsequently exacerbating interpersonal sensitivity and feelings of loneliness [39].
The most robust edge within the interpersonal sensitivity and loneliness network among college nursing students was observed between ‘Feel happy when someone compliments’ (IPSM-CS9) and ‘Make others happy’ (IPSM-CS10), both of which belong to the category of need for approval. The existence of this strong edge may be attributed to the characteristics of the nursing profession. Nursing students are required to engage in self-disclosure by demonstrating care, patience, and empathy to meet the emotional needs of patients, which leads to a desire for recognition and approval from others [40]. In addition to the influential edges in interpersonal sensitivity, several robust connections were identified within loneliness. The strongest link was observed between ‘Relational loneliness’ (T-ILS1) and ‘Social loneliness’ (T-ILS2). Initially, nursing students dedicate a substantial portion of their time and effort to academic studies and clinical training, which diminishes their availability for social interactions and the maintenance of relationships, consequently heightening their sense of social loneliness [41]. Furthermore, the nature of the nursing profession dictates that nursing students will encounter more difficulties and challenges [36]. They may experience relational loneliness when they lack support and understanding from their peers or teachers in the face of these challenges [41]. Based on our findings, meeting the need for approval among nursing students and providing social support to decrease feelings of social loneliness and relational loneliness may help alleviate issues related to interpersonal sensitivity and loneliness.
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In this interpersonal sensitivity and loneliness network among college nursing students, the most impactful bridge symptom was loneliness symptom of ‘Relational loneliness’ (T-ILS1), indicating that this particular symptom should be a focal point for interventions aimed at alleviating interpersonal sensitivity and loneliness. In one study, loneliness symptoms were recognized as playing a core role in the network of paranoia dimensions and mental health correlates [11]. Findings from a sample of the general population also revealed that well-being was more adversely associated with nodes linked to loneliness than with nodes associated with more severe psychopathological symptoms. One potential reason for this could be the pivotal role that relational loneliness may result in a lack of emotional support, social support, and a sense of belonging among nursing students, leading to the development of other forms of loneliness, such as social loneliness, and subsequently reducing their life satisfaction and quality of life [42]. Low social support and quality of life can further trigger interpersonal sensitivity [43]. Other impactful bridge symptoms encompassed the loneliness symptom of ‘Intimate loneliness’ (T-ILS3) and ‘Social loneliness’ (T-ILS2), indicating that these symptoms should also be addressed in interventions. Therefore, for nursing students, alleviating feelings of loneliness can potentially encourage them to actively engage in social activities, cultivate positive interpersonal relationships, and consequently reduce the occurrence of interpersonal sensitivity.
Based on previous studies, interpersonal sensitivity showed gender and urban-rural differences [31, 32], and patients with depression and anxiety had higher scores on the IPSM [33, 34]. The study conducted a network comparison test, which revealed significant differences in network structure invariance between rural and urban college nursing students, between individuals no anxiety symptoms and those having anxiety symptoms, and between individuals no depression symptoms and those having depression symptoms. This outcome can be explained by several factors. On one hand, urban areas, characterized by greater access to specialized healthcare and educational resources, may unwittingly affect the manifestation of interpersonal sensitivity and loneliness symptoms [44]. In contrast, rural regions often face resource constraints and a lack of mental health awareness could give rise to distinct symptom networks [45]. On the other hand, in comparison to college nursing students without anxiety and depression, those experiencing anxiety and depression may be more prone to over-interpret the behaviors or words of others, leading to distinct networks of interpersonal sensitivity and loneliness [46]. These findings suggest the need for tailored intervention measures based on the unique network structures for these two distinct groups of college nursing students. This particular discovery was not identified in the relevant studies utilizing network analysis and warrants further investigation in future research. Additionally, in other comparisons (such as no depression vs. having depression and male vs. female), no noteworthy differences were observed.
It is essential to acknowledge several potential limitations in our study. First, the network structure of interpersonal sensitivity and loneliness symptoms was established among college nursing students using cross-sectional data, which precluded the determination of causality among individual symptoms and exhibited restricted representativeness. Accordingly, the outcomes necessitate validation in forthcoming longitudinal studies. Secondly, self-reported assessments were employed to evaluate interpersonal sensitivity and loneliness symptoms, potentially introducing recall bias and limiting the ability to capture clinical phenomena accurately. Thirdly, it is necessary to acknowledge that methods for network analysis and development are evolving. Future studies should focus on such statistical optimization methods, as these may enhance the understanding of the results and facilitate deeper exploration of the research findings. Finally, although the current results underscore the interpersonal sensitivity and loneliness network among Chinese college nursing students, it is crucial to acknowledge that the applicability of these findings to additional populations may be constrained. Thus, as future study unfolds in the area, cumulative history analysis will be necessary.
Conclusion
In this study, centrality symptoms (i.e., ‘Feel happy when someone compliments’ and ‘They would not like me’) and bridge symptoms (i.e., ‘Relational loneliness’, ‘Intimate loneliness’ and ‘Social loneliness’) were recognized in this network of interpersonal sensitivity and loneliness among college nursing students. Intervening in the central symptoms may effectively enhance the self-confidence of nursing students and foster harmonious relationships with others, further facilitating better adaptation to interpersonal relationships. Furthermore, by overcoming bridge symptoms (e.g., meeting the need for approval and providing social support), nursing students can better adjust to nursing studies and practice with a more positive attitude during their college years.
Acknowledgements
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declarations
Ethics approval and consent to participate
This study was conducted following the principles outlined in the Declaration of Helsinki and the Measures for Ethical Review of Biomedical Research Involving Human Beings. Ethical approval was granted by the Ethics Review Committee of Jitang College, North China University of Science and Technology (JTXY-2022-002). In addition, students were informed about the study’s objectives and associated protocols. Signed informed consent was obtained from all participants, and data anonymization procedures were strictly adhered to.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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