Perceived stress and academic procrastination among higher vocational nursing students: the mediating roles of positive and negative emotions

This study used a cross-sectional design and recruited nursing students by convenient sampling from a higher vocational medical college in Langfang, Northern China, between June and July 2023. Full-time nursing students in grades 1 and 2 who voluntarily agreed to participate were included in the study, and those who had taken a leave of absence from school for any reason were excluded.

The required sample size was calculated using G*Power version 3.1 based on a priori power analysis [31]. It was based on a small effect size of 0.02, with an α of 0.05, and a power of 0.90. Given an effective response rate of 20%, the required minimum sample size was 1,235. Finally, 1,308 valid questionnaires were collected.

Before the investigation, the researchers contacted teachers or counsellors and distributed the questionnaire via WeChat, China’s most widely used social platform. The questionnaire was hosted on Wenjuanxing (https://​www.​wjx.​cn/​), a popular Chinese survey website. A QR code and link to the online questionnaire were sent to all nursing students through WeChat. The questionnaire included three sections: an introduction to the study’s purpose, a consent form, and the survey itself. Participants could only submit the questionnaire after completing all questions, and each student could submit it only once to prevent duplicates. This survey takes about 15 min to complete.

Personal demographic form

A personal demographic form was used to gather information about each participant’s age, gender, grade, residence, whether they were the only kid in the home, family economic status, and academic-related variables. Parenting style, whether participants had reset an exam, whether participants intended to advance their education, and the level of nursing professional identity were some of these characteristics.

The data were analysed using SPSS (version 26.0; SPSS Inc, Chicago, IL). Quantitative variables were described as means and standard deviations (SDs) and categorised variables were described as frequencies and percentages. A Pearson correlation coefficient was computed to examine the relationship between the four variables (perceived stress, positive emotions, negative emotions, and academic procrastination). Hierarchical regression models were carried out to evaluate the impact of the variables on academic procrastination.

The overall model (Fig. 1) was tested using the approach proposed by Preacher and Hayes, which allows multiple mediators to be included in the mediation analysis. We run the SPSS PROCESS for testing multiple mediation models with model 4 and 5000 bootstrap samples. Covariates (age, gender, grade, being an only child, residence, student cadre, family economic status, and having reset an exam) were controlled in this mediation analysis. A 95% confidence interval (CI) was estimated for the mediation effects, with CI not containing zero, indicating the mediation effects were significant. Harman’s single-factor test was used to examine the common method bias (CMB). The variance explained by the first factor was lower than 40%, indicating that no CMB exists in the research data.

The participants consisted of 1308 higher vocational college nursing students, with a mean age of 19.8 years and a range of 17–25 years. Among all participants, 87.2% were females, 50.1% were in their first year of study, 86.9% were not only children and 58.3% came from rural areas. Participants’ characteristics are presented in Table 1.

The results showed that the academic procrastination among nursing students in higher vocational college varied from 19 to 95, with a mean score of 50.36 ± 12.58, indicating a mild level of academic procrastination. The prevalence of mild, moderate, and severe academic procrastination was 57.5%, 22.0%, and 2.5%, respectively. Detailed score for each dimension and item is presented in Table 2.

Perceived stress and negative emotions were positively correlated with academic procrastination (r = 0.442, r = 0.376, all P < 0.001) and positive emotions were negatively associated with academic procrastination (r=-0.299, P < 0.001). The correlation coefficients for perceived stress, positive emotions, negative emotions and academic procrastination are presented in Table 3.

Univariate analysis was tested for differences in demographic and academic-related characteristics among academic procrastination. The results showed that gender, residence, and having resat an exam were significantly different among academic procrastination (P < 0.05). The results were presented in the Table 1.

As shown in Table 4, the hierarchical linear regression analysis indicated that having reset an exam was significantly correlated with academic procrastination (P < 0.01). After controlling covariates, perceived stress, positive emotions, and negative emotions were related to academic procrastination (β = 0.252, β=−0.167, β = 0.214, P < 0.001; respectively). All variables explained 25.6% of the variance in academic procrastination.

The Herman single-factor analysis was carried out to examine CMB, and items of the four variables, i.e., perceived stress, positive emotions and negative emotions, and academic procrastination were examined by non-rotation principal component factor analysis. The first factor resulted in 22.2% of variance lower than 40%, indicating that there was no CMB in this study.

Figure 2 reveals that the standardized coefficient of perceived stress on academic procrastination decreased from 0.445 (95% CI (0.397, 0.494)) to 0.252 (95% CI (0.188, 0.317)), which was still statistically significant, after introducing the mediator of positive emotions and negative emotions into the model with controlling covariates. This result suggested a partial mediation and the relationship between perceived stress and academic procrastination was partially mediated by positive and negative emotions. The indirect effect of perceived stress on academic procrastination through positive emotions was 0.072 (95% CI (0.046, 0.101)) with an effect size of 16.2%, while through negative emotions was 0.121 (95% CI (0.084, 0.162)) with an effect size of 27.2%. The ratio of the direct effect and indirect effects to the total effect is presented in Table 5.

The study indicated that academic procrastination is prevalent among vocational nursing students, with 82.0% experiencing it to varying degrees, and 24.5% showing moderate to severe procrastination, aligns with the findings reported by Ren et al. [37]. Multiple regression analysis demonstrates significant associations of gender and course retakes on academic procrastination among these students. Male students exhibit higher levels of procrastination than female students, corresponds to the conclusions of Zhu [38] and Jia [39]. This may be attributed to societal norms that encourage males to display greater independence and autonomy. These role expectations may lead to reluctance to seek help when facing difficulties and academic challenges, thereby increasing the likelihood of academic procrastination [40]. Additionally, male nursing students may encounter a lack of peers in the current university environment, which may hinder communication and support during their studies. Compared to female nursing students, they might engage more in gaming and online activities, which can detract from time dedicated for academic tasks, further contributing to procrastination [38]. Students with a history of course retakes demonstrate increased levels of academic procrastination, possibly due to poor time management, reduced confidence following failure, and ineffective learning strategies [41]. The additional stress and anxiety from retaking courses could further prompt avoidance behaviours, exacerbating academic procrastination. Thus, educators should prioritize targeted interventions for these two groups.

The study identified significant correlations between perceived stress, positive and negative emotions [42], and academic procrastination. Perceived stress and negative emotions are positively correlated with academic procrastination, while positive emotions were negatively correlated, aligning with previous research findings [26, 43, 44]. These findings imply that individuals experiencing higher perceived stress are less inclined to approach academic tasks promptly, leading to increased procrastination. Students experiencing higher levels of positive emotions exhibit reduced academic procrastination, likely due to the energizing and confidence-boosting effects of positive emotions, which facilitate engagement in tasks and thus reduce procrastination [45]. Conversely, students experiencing negative emotions may engage in short-term emotional regulation strategies, such as gaming or watching short videos, as a means to temporarily alleviate anxiety and helplessness, potentially contributing to academic procrastination [44]. Hence, targeting the reduction of perceived stress, promoting positive emotional experiences, and reducing the impact of negative emotions (e.g., tension and loss of control) among vocational nursing students may effectively reduce academic procrastination.

This study found that positive and negative emotions partially mediated the relationship between perceived stress and academic procrastination. Consistent findings were reported by Deng et al. [46], who provided evidence supporting the significant mediating role of negative emotions in the relationship between academic stress and sleep quality among Chinese adolescents. Similarly, Alkharj et al. [47] explored the relationship between stress and academic engagement among Saudi undergraduate nursing students, emphasizing the critical role of emotional processes in mediating stress-related academic outcomes. Distinguishing itself from these studies, our research contributes to the existing literature by concurrently examining both positive and negative emotions as mediators, offering a thorough understanding of their distinct impacts.

The findings of this study can be explained through the cognitive appraisal theory of stress, which emphasizes the dynamic interplay between stress appraisal and coping strategies [18]. According to this theory, when students perceive academic tasks as overwhelming and beyond their capabilities, they tend to experience heightened stress levels accompanied by negative emotional responses, such as anxiety and frustration. These stress-induced emotional changes often trigger maladaptive coping mechanisms, leading to negative behaviours such as academic procrastination [18]. Building on this framework, emotion regulation theory may provide deeper insights into the relationship between emotional changes and coping behaviours [22]. Emotion regulation is categorized into two types: antecedent-focused regulation and response-focused regulation [22]. Antecedent-focused regulation involves modifying one’s cognitive appraisal of a situation to sustain positive emotions. However, when students appraise academic tasks as threats rather than challenges, the generation and maintenance of positive emotions may be impeded. In addition, prolonged exposure to high-pressure environments can lead to emotional resource depletion, further inhibiting the generation of positive emotions [48]. Previous studies have indicated that the decline in positive emotions can weaken students’ ability to enhance intrinsic motivation, restore psychological resources and maintain problem-focused coping strategies [23, 24]. Consequently, students may struggle to actively seek solutions to academic challenges, which ultimately contributes to academic procrastination. Furthermore, response-focused regulation occurs after emotional responses have already been triggered and typically involves strategies such as expression suppression [22]. While expression suppression may temporarily alleviate emotional distress, it fails to address the underlying sources of stress [25]. This unresolved emotional burden can accumulate over time, depleting cognitive and environmental resources and thereby reinforcing avoidant behaviours such as academic procrastination.

The study further revealed that the indirect effect of perceived stress on academic procrastination through positive emotions was quantified with an effect size of 16.2%, whereas the indirect effect through negative emotions had a larger effect size of 27.2%. This finding suggests that negative emotional responses to stress may play a more significant role in academic procrastination than positive emotional responses. Therefore, educators should pay greater attention to helping students manage their negative emotions.

To reduce academic procrastination among vocational nursing students, educators should implement strategies that focus on managing stress, negative emotions, and promoting positive emotional experiences. Regular assessment of common stressors faced by students is crucial to identify the areas where intervention is most needed. Stress-reduction techniques, such as deep breathing exercises and mindfulness meditation, should be integrated into the curriculum to directly alleviate stress. For example, educators could introduce programs like Mindfulness-Based Stress Reduction, which can help students manage stress and improve emotional well-being. Additionally, educators and mental health professionals should help students develop healthier emotional regulation strategies, such as emotional intelligence training. Emotional intelligence training can include courses on “emotional awareness”, which help students identify and understand their emotions and teach them how to manage these emotions. The training can also involve cognitive reappraisal training and emotional expression programs, which may help students reframe their perceptions of academic challenges, sustain positive emotions, and mitigate the impact of negative emotions on their academic procrastination.

The limitations of this study should be acknowledged. First, as a cross-sectional study, it examined association rather than establishing causality among perceived stress, positive and negative emotions, and academic procrastination. Future research should employ cross-lagged designs and experimental methods to investigate the causal mechanisms underlying these relationships. Second, the use of convenience sampling and dada collection from one higher vocational college may limit the generalizability of the findings. Future research should include more higher vocational colleges from different regions, and increase the sample size to enhance the generalizability of the results. Thirdly, although WeChat is widely used, its reliance may exclude students who are inactive or do not use the platform, potentially limiting sample diversity and generalizability. Future research could use multiple platforms and methods (such as email or other campus tools) to enhance sample representativeness and external validity. Finally, this study relied on self-reported measures, which may introduce recall bias and social desirability bias. Therefore, future research should incorporate face-to-face qualitative interviews to enhance the realism and reliability of the findings.