The relationships among academic self-efficacy, educational stress, and academic motivation in nursing students: a structural equation modeling analysis

Abstract

Background

Academic self-efficacy, educational stress, and academic motivation are key psychological factors influencing nursing students’ academic performance and learning experiences. However, the relationships among these variables have not been sufficiently examined in nursing education.

Aim

This study aimed to examine the relationships among academic self-efficacy, educational stress, and academic motivation in nursing students.

Methods

This study was conducted with nursing students at Fırat University Faculty of Health Sciences in Elazığ, Turkey, during the autumn semester of the 2023–2024 academic year. A total of 519 nursing students were included in this cross-sectional correlational study. Data were collected using a personal information form, the Academic Self-Efficacy Scale, the Educational Stress Scale, and the Academic Motivation Scale. The data were analyzed using structural equation modeling (SEM).

Results

The mean levels indicated that academic self-efficacy and academic motivation were moderate to high, while educational stress was at a moderate level.The results of the SEM indicated that academic self-efficacy was significantly associated with educational stress (β = -0.329, p < .001) and academic motivation (β = 0.621, p < .001). Educational stress was also significantly associated with academic motivation (β = 0.197, p < .001). In addition, educational stress partially mediated the relationship between academic self-efficacy and academic motivation (β = -0.063, p < .01). The total effect of academic self-efficacy on academic motivation was significant (β = 0.560, p < .001). The model explained 10% of the variance in educational stress and 34% of the variance in academic motivation.

Conclusions

The findings indicated that academic self-efficacy plays an important role in reducing educational stress and increasing academic motivation among nursing students. Educational stress showed a positive association with academic motivation and partially mediated the relationship between academic self-efficacy and academic motivation. These results suggest that strengthening academic self-efficacy may improve students’ motivation and stress levels. Future studies should use longitudinal and multi-center designs to confirm these relationships.

Introduction

Nursing education is a rigorous process that integrates complex theoretical knowledge with high-stakes clinical practice [1]. In the modern healthcare landscape, the increasing demand for high-quality care necessitates nurses who possess not only advanced clinical skills but also high levels of academic self-efficacy (ASE) and professional motivation [2].

ASE refers to an individual’s belief in their ability to successfully perform academic tasks [3]. In nursing education, strong ASE enables students to cope with challenges, adapt to demanding learning environments, and achieve higher academic performance, whereas low ASE may lead to reduced coping capacity, negative emotional outcomes, and decreased motivation [4]. Recent evidence indicates that ASE is a key determinant of academic success and learning engagement and is significantly associated with perceived stress and psychological outcomes among nursing students [5–7].

Stress is a dynamic process in which demands are perceived as exceeding an individual’s coping resources [8]. Among nursing students, stress is a significant concern due to both academic and clinical demands. In nursing education, stress is further intensified by clinical responsibilities, direct patient care, fear of making errors, and high expectations for professional competence. In addition to clinical stressors, academic factors such as heavy workload, time pressure, examinations, and performance expectations substantially contribute to educational stress [9]. Nursing students are therefore exposed to multiple and overlapping stressors, increasing their vulnerability to psychological distress and burnout [6]. This combination of academic and clinical demands has been associated with negative outcomes such as anxiety, emotional exhaustion, reduced well-being, and decreased academic performance [10]. However, although excessive stress is associated with negative academic and psychological outcomes, some studies suggest that moderate levels of stress may have a facilitating effect on motivation and performance, depending on individual coping resources and contextual factors [8, 11]. In this context, academic self-efficacy may play a crucial role in determining whether stress is perceived as a motivating challenge or a debilitating burden depending on individual cognitive appraisal processes [12, 13].

Academic motivation is a key determinant of learning, persistence, and professional development in nursing education [14]. Higher motivation is associated with improved academic outcomes, stronger self-confidence, and reduced academic distress [15]. Evidence suggests that students with higher ASE are more likely to adopt effective learning strategies and maintain stronger motivation levels, indicating a close interrelationship among these constructs [16].

The relationships among academic self-efficacy, educational stress, and academic motivation can be understood within the framework of social cognitive theory. According to this theory, self-efficacy plays a central regulatory role in shaping how individuals perceive and respond to stressors [12]. Students with higher levels of academic self-efficacy are more likely to interpret stress as a manageable challenge and sustain their motivation, whereas those with lower self-efficacy may perceive stress as a threat, leading to decreased motivation and poorer academic outcomes [11–13]. This perspective highlights the interconnected nature of these variables and suggests that stress may function as a potential mediating mechanism in explaining the relationship between self-efficacy and academic motivation.

Although the relationships between self-efficacy, stress, and motivation have been widely studied, most existing research has examined these constructs separately or within different educational contexts. Evidence on the simultaneous and structural relationships among these variables in nursing students remains limited. Therefore, this study aims to examine the relationships among academic self-efficacy, educational stress, and academic motivation, thereby contributing to a more comprehensive understanding of these factors and supporting evidence-based improvements in nursing education and practice. The proposed model for the study is illustrated in Fig. 1.

Fig. 1.

Proposed model

Research questions

RQ1. What is the relationship between academic self-efficacy, educational stress, and academic motivation in nursing students?

RQ2. Does educational stress mediate the relationship between academic self-efficacy and academic motivation in nursing students?

Method

Study desing

In the current study, a cross-sectional survey design was employed using a structural equation modeling (SEM) approach. SEM is a statistical methodology used to test theoretical models based on observed variables [17]. It provides flexibility in hypothesis testing [18] The conceptual model of this study is based on social cognitive theory. It proposes that academic self-efficacy influences both educational stress and academic motivation. In addition, educational stress is considered a mediating variable in the relationship between academic self-efficacy and academic motivation. Accordingly, higher academic self-efficacy is expected to reduce educational stress and increase academic motivation, while educational stress may also affect academic motivation.

Setting and participants

This study was conducted with nursing students at Fırat University Faculty of Health Sciences in Elazığ, Turkey, during the autumn semester of the 2023–2024 academic year. The total population consisted of 741 students. The inclusion criteria were being aged 18 years or older, being actively enrolled in the nursing program, and volunteering to participate. The exclusion criteria included students who were on leave during the data collection period or those who returned incomplete survey forms. Rather than selecting a specific sub-sample, the study aimed to reach the entire population. Ultimately, 519 students (a response rate of 70%) completed the study. To verify the adequacy of the sample size, a post-hoc power analysis was performed using G*Power (version 3.1.9.4) software. With a sample of 519 participants, the analysis revealed a statistical power of 0.95, an effect size of 0.145, and a significance level of 0.05. These results confirm that the sample size was sufficient to ensure the reliability and validity of the study findings [19].

Instruments

The scales that have already demonstrated reliability and validity in the Turkish context were employed to evaluate students’ individual perceptions. Detailed information regarding the scales is provided below.

Personal information form

“A Personal Information Form was used to collect demographic data (age, gender, marital status, educational status, and grade level).

ASE

The scale used in this study was originally developed by Jerusalem and Schwarzer (1981) to assess university students’ ASE [20]. It was later adapted to Turkish language and culture by Yılmaz et al. (2007) [21]. The scale consists of items rated on a 4-point Likert scale, with response options ranging from “4 = Completely fits me” to “1 = Does not fit me at all.” Notably, the 7th item is reverse-scored. Higher scores on the scale indicate greater ASE among students. In the original adaptation study, the Cronbach’s Alpha coefficient was reported as 0.79, while in this study, it was calculated as 0.70. To evaluate the construct validity of the scale, confirmatory factor analysis (CFA) was conducted, yielding satisfactory model fit indices (χ2 (97) = 3.09; RMSEA = 0.063; CFI = 0.915; TLI = 0.905; SRMR = 0.052).

Education stress scale (ESS)

ESS was developed by Sun et al. (2011) [22]. Çelik et al. (2014) to measure educational stress among university students and was adapted into Turkish by Çelik et al. (2014) [23]. The scale consists of 16 items across five sub-dimensions (work pressure, workload, grade anxiety, self-expectation, and hopelessness), rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Total scores range from 16 to 80, with higher scores indicating higher educational stress. The scale does not include reverse-coded items. In the current study, Cronbach’s alpha was 0.85. CFA indicated a good model fit (χ² (11) = 2.16, RMSEA = 0.047, CFI = 0.987, TLI = 0.974, SRMR = 0.026).

Academic Motivation Scale (AMS)

AMS developed by Bozanoğlu (2004) was used to assess students’ academic motivation levels [24]. The scale consists of 20 items rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). It is composed of three sub-dimensions: self-transcendence (seven items), knowledge utilization (six items), and exploration (seven items). One item (item 4) is reverse-coded. Total scores range from 20 to 100, with higher scores indicating higher academic motivation. In the present study, Cronbach’s alpha was 0.90 (0.88 in the original study). CFA demonstrated an acceptable model fit (χ² (160) = 3.89, RMSEA = 0.075, CFI = 0.932, TLI = 0.917, SRMR = 0.060).

Collecting the data

The data collection process comprised the utilization of a personal information form, along with three distinct scales, namely the ASE Scale, the ESS, and the AMS. The prepared questionnaire form was distributed to the students online. Over 15 days, the students were expected to complete the questionnaires. A total of 532 students filled out the questionnaires. Data collection was stopped when no one filled out the questionnaires for three consecutive days. The return rate for the questionnaires was determined to be 71%. On average, it took approximately 10–12 min to complete the questionnaire form. When reviewing the completed questionnaires, it was discovered that in some cases, at least half of the questions were left unanswered (n = 7). Additionally, in a few cases, the same answer was given for all questions (n = 3). These incomplete and repetitive forms were excluded from the data. Ultimately, a total of 519 questionnaires were included in the study.

Analysing the data

Descriptive statistical analyses were conducted using IBM SPSS Statistics version 22.0 (IBM Corp., Armonk, NY, USA), while CFA and SEM were performed using Mplus version 8.3 (Muthén & Muthén, Los Angeles, CA, USA). Since the data were collected through student self-reports, the potential for common method bias needed to be addressed [25]. This bias occurs when the measurement tools, rather than the actual variables, account for variance in the dataset, potentially distorting the relationships between variables. To evaluate this, Harman’s (1967) single-factor test was applied. The findings revealed that a single-factor model accounted for less than 50% of the total variance, while three factors with eigenvalues exceeding 1.0 explained 57.56% of the variance, indicating minimal common method bias [25, 26].

To assess reliability, McDonald’s Omega, Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE) were calculated for each variable. Internal consistency was considered acceptable when Cronbach’s alpha and McDonald’s Omega coefficients were 0.70 or higher [27, 28]. Convergent validity was evaluated using CR values above 0.70, with CR greater than AVE and AVE exceeding 0.50 [29] Descriptive statistics, including mean scores, standard deviations, and correlation analyses, were computed. The construct validity of the scale was examined through CFA. Model fit was evaluated using multiple indices: chi-square divided by degrees of freedom (χ²/df), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI). Model fit was deemed acceptable when χ²/df was below 5, with values under 3 considered a good fit. Additionally, RMSEA and SRMR values below 0.08, along with CFI and TLI scores above 0.90, were used as indicators of a well-fitting model [18, 30].

The relationships between variables were analysed by SEM analysis using Bayesian estimator. Bayesian estimation is one of the three options available in Mplus for handling ordinal categorical data. The other two options, maximum likelihood estimator (MLR) and robust weighted least squares estimator (WLSMW) [31]. The MLR estimator is not ideal in this study because the three scales used provide more than four threshold factors, which leads to a need for a large number of numerical integration dimensions and consumes significant time and storage space. Additionally, the WLSMV estimator is not advantageous due to the presence of 44 variables/items in the analysis, which is close to the 50-variable threshold. As the number of variables increases, the weight matrix grows exponentially and becomes computationally burdensome. In contrast, Bayesian statistics offer the advantage of incorporating prior knowledge to improve the accuracy of indirect effects compared to classical estimation and testing methods [32]. The study required numerical integration for direct and indirect relationships. For analyses requiring numerical integration, the Bayesian estimator was used due to its high performance [33]. The Bayesian estimator provides more robust predictions in small samples compared to frequentist estimators, and it offers confidence interval (CI) coverage [34]. One advantage of the Bayesian estimator is its ability to provide a Markov chain Monte Carlo (MCMC) approximation of Metropolis-Hastings for ordered categorical data with three or more categories [31]. A 95% CI was reported for each latent variable, and estimates were considered significant if the CI did not include zero [35].

It is essential to recognize that the Bayesian estimator does not generate conventional fit indices. Instead, model evaluation depends on analyzing the potential scale reduction (PSR) value and parameter scatter plots [32]. A PSR value below 1.05 indicates model convergence, suggesting that the model is acceptable [36]. Additionally, during the estimation process, the distribution of parameters should be closely monitored. If significant fluctuations are observed, further iterations may be necessary to ensure stability [32]. To better understand the R² explanation percentages, Cohen’s f² analysis findings were analyzed. Cohen’s f² value was interpreted as small for f²≥0.02, medium for f²≥0.15, and large for f²≥0.35 [37].

Ethical considerations

Ethical approval and informed consent were obtained prior to data collection.

Results

Among the students who participated in the study, approximately 74% (n = 384) were female, while 26% (n = 135) were male. Their ages ranged from 18 to 45 years, with a mean age of 20.75 years (SD = 2.593). The participants included 29.9% (n = 155) 1st-grade students, 26.6% (n = 138) 2nd-grade students, 20% (n = 104) 3rd-grade students, and 23.5% (n = 122) 4th-grade students (Table 1).

Table 1.

Demographic Characteristics of Students (n = 519)

Veriables		n	%
Gender	Famale	384	74
	Male	135	26
Class	1st grade	155	29.9
	2 nd grade	138	26.6
	3 rd grade	104	20.0
	4 th grade	122	23.5
Continuous variables		Mean ± Sd	Min-max
Age		20.75 ± 2.593	18–45

The results section is divided into two main parts: Preliminary Analyses and Bayesian Mediation Analysis. In the Preliminary Analyses, descriptive statistics and correlation analyses were conducted to explore the relationships between variables, including ASE, educational stress, and motivation. The Bayesian Mediation Analysis examined both the direct and indirect effects among these variables, employing a Bayesian estimator model to verify the mediating role of motivation and assess the overall fit of the structural model.

Preliminary analyzes

The statistical analysis of the variables indicates that students’ ASE had a moderate level, with a mean score of 2.59 (± 0.45). Similarly, educational stress (3.11 ± 0.66), work pressure (3.03 ± 0.68), workload (2.96 ± 0.82), grade anxiety (3.13 ± 0.92), and self-expectation (2.86 ± 0.085) also fell within the moderate range. However, the mean score for hopelessness (3.53 ± 0.086) was notably high. In contrast, academic motivation (3.40 ± 0.55), self-transcendence (3.44 ± 0.57), and exploration (3.57 ± 0.62) exhibited high levels, while knowledge utilization (3.15 ± 0.70) remained in the moderate category. The relatively low standard deviation values suggest that students’ responses were fairly consistent.

Correlation analysis results indicated a weak negative association between the ASE Scale and ESS (r = − .16, p < .01) and a moderate positive link between the ASE Scale and the Academic Motivation Scale (r = .45, p < .01). No statistically significant correlation was found between the Educational Stress Scale and the Academic Motivation Scale (r = .03, p > .05). Reliability analyses, including Cronbach’s alpha, McDonald’s omega, composite reliability (CR), and average variance extracted (AVE), demonstrated that Cronbach’s alpha, McDonald’s omega, and CR values surpassed the 0.70 threshold, while AVE values exceeded 0.50 (Table 2).

Table 2.

Preliminary analysis results (n = 519)

Variable	M (SD)	CR	AVE	α	ω	ASE	ESS	PfS	Wload	WaG	S-exp	Desp	AMS	Strend	UoK	Exp
ASE	2.59 (0.45)	0.78	0.52	0.70	0.97	1
ESS	3.11 (0.66)	0.90	0.63	0.86	0.85	− 0.16**	1
PfS	3.08 (0.68)	0.75	0.51	0.71	0.73	− 0.11*	0.76**	1
Wload	2.96 (0.82)	0.81	0.54	0.74	0.75	− 0.09*	0.76**	0.62**	1
WaG	3.13 (0.92)	0.72	0.53	0.71	0.72	0.01	0.68**	0.31**	0.36**	1
S-exp	2.86 (0.85)	0.78	0.52	0.72	0.73	− 0.25**	0.77**	0.47**	0.49**	0.37**	1
Desp	3.53 (0.86)	0.89	0.62	0.79	0.79	− 0.16**	0.76**	0.42**	0.41**	0.47**	0.55**	1
AMS	3.40 (0.55)	0.91	0.78	0.90	0.89	0.45**	0.03	− 0.03	− 0.04	0.21**	− 0.13**	0.09*	1
Strend	3.44 (0.57)	0.75	0.51	0.71	0.70	0.35**	0.03	− 0.04	− 0.07	0.25**	− 0.15**	0.10*	0.87**	1
UoK	3.15 (0.70)	0.79	0.52	0.78	0.77	0.39**	0.09	0.05	0.02	0.18**	0.05	0.12**	0.89**	0.66**	1
Exp	3.57 (0.62)	0.82	0.57	0.80	0.80	0.43**	− 0.03	− 0.07	− 0.06	0.15**	− 0.15**	0.02	0.89**	0.67**	0.69**	1

ASE: Academic Self-Efficacy Scale; ESS: Educational Stress Scale; Pfs: Work Pressure; Wload: Workload; WaG: Grade Anxiety; S-exp: Self Expection; Desp: Hopelessness; AMS: Academic Motivation Scale; Strend: Self-transcendence; Uok: knowledge utilization; Exp: Exploration

Bayesian mediation analysis

The Bayesian estimator achieved model convergence after 4,400 iterations, with the PSR value decreasing to 1.048, below the 1.05 threshold. However, noticeable fluctuations were observed in the trace plots (Fig. 2). To address this, we employed the TECH8 command to increase the number of iterations and re-evaluated the PSR values. At 25,000 iterations, the fluctuations in the trace plots were significantly reduced, confirming that the model was stable and should not be rejected [33].

Fig. 2.

Parameter trace plot and histograms with fbiterations ranging from 4.400 to 25.000

The direct effect between ASE and Estress was negative and significant (β = − 0.329, 95% CI [-0.423, − 0.227]). The direct effect between ASE and Mot was positive and significant (β = 0.621, 95% CI [0.534, 0.698]), and the direct effect between Estress and Mot was also positive and significant (β = 0.197, 95% CI [0.095, 0.297]). The indirect association of ASE with Mot via Estress was negative and significant (β = − 0.063, 95% CI [-0.025, − 0.117]). The total effect of ASE on Mot (β = 0.560, 95% CI [0.484, 0.636]) was positive and significant. This result indicates a partial mediating role of Mot. The structural model established within the scope of the study explains 10.8% of the variance in Estress and 34.6% of the variance in Mot. Cohen’s f² analysis showed that the level of ASE explaining Estress (f² = 0.12) was small, while the level of ASE and Estress together explaining Mot (f² = 0.529) was large (Table 3). SEM for the study is illustrated in Fig. 3.

Table 3.

Standardized results for bayesian mediation model

					95% CI
Direct Effect	β		S.E.	z	Lower	Upper	p	R 2
ASE→ESS	− 0.329		0.049	-6.71	− 0.423	− 0.227	< 0.001	0.108
ASE→AMS	0.621		0.042	14.78	0.534	0.698	< 0.001
Estress →ESS	0.197		0.052	3.79	0.095	0.297	< 0.001
Indirect Effect
ASE→ESS→AMS	− 0.063		0.021	-3.07	− 0.025	− 0.117	< 0.001
Total Effect
ASE→ AMS	0.560		0.039	14.29	0.484	0.636	< 0.001	0.346

ASE: Academic Self-Efficacy Scale; ESS: Educational Stress Scale; AMS: Academic Motivation Scale

Fig. 3.

Structural equation model. Academic Self-Efficacy Scale; ESS: Educational Stress Scale; Pfs: Work Pressure; Wload: Workload; WaG: Grade Anxiety; S-exp: Self Expection; Desp: Hopelessness; AMS: Academic Motivation Scale; Strend: Self-transcendence; Uok: knowledge utilization; Exp: Exploration

Discussion

The present study examined the relationships among ASE, educational stress, and academic motivation in nursing students using a Bayesian structural equation modeling approach. The findings support an integrated model in which ASE plays a central role in shaping both educational stress and academic motivation.

The first finding demonstrated that ASE is associated with lower educational stress among nursing students. This result is consistent with Social Cognitive Theory, which emphasizes self-efficacy as a key determinant of individuals’ ability to manage environmental demands [12]. Recent empirical evidence strongly supports this relationship in nursing education. For example, Nazari et al. (2025) found that higher academic self-efficacy is associated with lower academic stress among undergraduate nursing students, indicating its protective role in managing academic demands [38]. Similarly, Shubayr and Dailah (2025) reported that nursing students with higher self-efficacy experience lower perceived stress during clinical practice, highlighting the importance of self-efficacy in stressful clinical environments [39].In addition, Özsaker et al. (2025) demonstrated a significant inverse relationship between academic self-efficacy and perceived stressors in clinical settings [5], while Andargeery et al. (2025) found that lower self-efficacy is associated with higher psychological distress and academic burnout among nursing students [6]. Hamka et al. (2025) further supported these findings by showing that self-efficacy, along with psychosocial resources, plays a significant role in reducing academic stress [40].Overall, these findings suggest that nursing students with higher ASE are more likely to perceive academic and clinical challenges as manageable, thereby experiencing lower levels of educational stress and demonstrating better psychological adaptation to academic demands.

The second finding indicated that ASE has a strong positive effect on academic motivation. This result is consistent with Social Cognitive Theory, which proposes that individuals with higher self-efficacy demonstrate greater persistence and goal-directed behaviour [13]. Empirical studies have also confirmed this relationship, showing that ASE is strongly associated with academic motivation and engagement among nursing students [3, 5]. Furthermore, Kordsalarzehi et al. (2025) demonstrated that interventions aimed at improving academic skills can significantly enhance both self-efficacy and motivation [41]. These findings indicate that ASE is a critical psychological resource that strengthens students’ confidence and academic engagement.

Another important finding of this study is that educational stress has a small but significant effect within the structural model. This result should be interpreted within a context-dependent framework. According to the Yerkes–Dodson law, moderate levels of stress may enhance performance and motivation, whereas excessive stress leads to performance decline [42]. Recent studies also suggest that academic stress may have a facilitating role in motivation when it remains at manageable levels [11, 43]. However, higher levels of stress are consistently associated with burnout, reduced academic performance, and psychological distress [9, 10]. Therefore, the present findings indicate that educational stress may have a limited motivational contribution; however, its effect is weak compared to the dominant influence of academic self-efficacy.

Finally, the mediation analysis revealed that educational stress partially mediates the relationship between ASE and academic motivation. The indirect effect was negative, indicating that higher ASE reduces educational stress, which in turn slightly diminishes the overall positive pathway from ASE to motivation. However, the total effect remained strong and positive, demonstrating that ASE is the dominant predictor of academic motivation. This pattern indicates a competitive (inconsistent) mediation structure, where educational stress functions as a secondary and indirect mechanism rather than a primary explanatory pathway. Recent structural equation modeling studies similarly suggest that self-efficacy influences academic outcomes through complex psychological pathways involving stress appraisal, coping strategies, and motivational regulation [6, 44, 45]. Overall, the findings support an integrated model in which academic self-efficacy serves as the central factor shaping both stress perception and academic motivation in nursing students.

Practical implications

These findings have significant practical implications for nursing education and curriculum development. Educators should prioritize interventions that enhance students’ academic self-efficacy, as it is the primary driver of motivation and a key buffer against distress. Training programs should incorporate mastery experiences and peer-modeling to build clinical confidence and problem-solving skills. Furthermore, since moderate stress can facilitate motivation, nursing curricula should not only focus on reducing stress but also on teaching cognitive appraisal techniques that help students transform academic pressure into a productive motivational tool. Strengthening these psychological resources will ultimately support the development of more resilient and motivated nursing professionals.

Limitations

This study has several limitations. First, its cross-sectional design limits causal inferences and restricts the findings to associations between variables. Second, the use of self-report questionnaires may introduce response bias. Third, the study was conducted in a single center, which limits the generalizability of the findings. Finally, contextual and temporal factors may influence students’ stress, motivation, and educational experiences. Therefore, the results should be interpreted with caution. To minimize these limitations, validated instruments were used, and data collection was conducted under standardized and anonymous conditions. Future studies are recommended to use longitudinal designs and include multiple centers to enhance generalizability and better examine causal relationships among variables.

Conclusion

This study demonstrates that academic self-efficacy is a key determinant of academic adjustment among nursing students, showing a strong positive association with academic motivation and a significant negative association with educational stress. The findings indicate that students with higher self-efficacy are better able to manage academic demands, resulting in lower perceived stress levels and higher motivation.Educational stress showed a small but significant positive association with academic motivation, suggesting that moderate levels of stress may contribute to students’ motivational engagement; however, this effect remains weak compared to the dominant influence of academic self-efficacy. The mediation analysis further revealed that educational stress partially mediates the relationship between academic self-efficacy and academic motivation, indicating that self-efficacy enhances motivation both directly and indirectly through its effect on stress levels. Overall, these findings highlight academic self-efficacy as the primary psychological resource in nursing education. Therefore, educational strategies aimed at strengthening students’ self-efficacy may be effective in reducing stress and enhancing academic motivation, ultimately supporting academic success and professional development.

Abbreviations

ASE

Academic self-efficacy

ESS

Educational stress scale

AMS

Academic motivation scale

SEM

Structural equation modeling

χ²

Chi-square

RMSEA

Root mean square error of approximation

CFI

Comparative fit index

TLI

Tucker–lewis index

SRMR

Standardized root mean square residual

Author contributions

GB: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Supervision MA: Conceptualization Investigation, Writing – original draft, Writing SK: Conceptualization Investigation, Writing – original draft, Writing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Fırat University Scientific Research Projects Coordination Unit (FUBAP) under project number SYO.25.15.

Data availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Ethics Committee of Fırat University Social Sciences and Humanities Research (dated 14 December 2023; approval no: 2023/23). Participants were informed that participation was voluntary, and written informed consent was obtained from all participants prior to data collection. The study was conducted in accordance with the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly ensured, and all data were used only for research purposes.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.