Heterogeneity Study on Learning Strategies of Undergraduate Nursing Students Based on Latent Profile Analysis and Exploration of Influencing Factors

Heterogeneity Study on Learning Strategies of Undergraduate Nursing Students Based on Latent Profile Analysis and Exploration of Influencing Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Heterogeneity Study on Learning Strategies of Undergraduate Nursing Students Based on Latent Profile Analysis and Exploration of Influencing Factors Ting Luo, Yuhang Chen, yan Huang, qin Xu, Chaoya Hu, xiaoxia Lin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7821396/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Learning strategies are critical for undergraduate nursing students to acquire professional knowledge, develop clinical competencies, and adapt to the demands of future nursing practice. However, existing research often treats learning strategies as a homogeneous construct, overlooking potential heterogeneity in how nursing students employ these strategies. Identifying latent profiles of learning strategies and their influencing factors is essential to develop targeted educational interventions, optimize learning outcomes, and promote the professional development of nursing students. Methods A cross-sectional study was conducted between January and March 2025. Convenience sampling was used to recruit 2,279 undergraduate nursing students from three universities in Sichuan Province, China. Data were collected using four validated instruments: a general information questionnaire, the College Students’ Learning Strategy Usage Scale, the College Students’ General Academic Emotion Scale, and the Academic Self-Efficacy Scale. Latent Profile Analysis (LPA) was performed to identify distinct latent profiles of learning strategies, with model selection based on fit indices and theoretical plausibility. Multivariate logistic regression analysis was then used to explore the associations between academic emotion, academic self-efficacy, and latent profile membership, adjusting for potential confounding variables. Results LPA revealed three distinct latent profiles of learning strategies among undergraduate nursing students: (1) Poor Learning Strategy Class (n = 107, 4.69%), characterized by low scores across all learning strategy dimensions; (2) Good Learning Strategy Class (n = 1,301, 57.08%), with moderate scores in most strategy dimensions and adequate metacognitive awareness; and (3) Excellent Learning Strategy Class (n = 870, 38.17%), defined by high scores in all learning strategy domains, particularly in deep learning and self-regulated learning. Logistic regression analysis showed that academic emotion (OR = 0.906, 95% CI: 0.876–0.937, P < 0.001) and academic self-efficacy (OR = 1.018, 95% CI: 1.012–1.024, P < 0.001) were significant predictors of latent profile membership. Specifically, more positive academic emotions and higher academic self-efficacy were associated with a greater likelihood of belonging to the Excellent Learning Strategy Class (vs. the Poor or Good classes). Conclusions Undergraduate nursing students exhibit significant heterogeneity in learning strategies, which can be classified into three distinct latent profiles. Academic emotion and academic self-efficacy are key factors influencing these profiles. Nursing educators should prioritize targeted interventions for students in the Poor Learning Strategy Class (to build foundational learning skills) and Good Learning Strategy Class (to enhance deep learning and self-regulation). Such interventions could include academic emotion management workshops (e.g., stress reduction techniques, fostering positive learning attitudes) and self-efficacy enhancement programs. By addressing these influencing factors, nursing education can optimize students’ learning strategies, ultimately improving their academic performance and supporting the development of competent, well-rounded nursing professionals. Undergraduate nursing students Learning strategies Latent Profile Analysis (LPA) Academic emotion Academic self-efficacy Figures Figure 1 Introduction Against the backdrop of the continuous upgrading of global healthcare systems and the increasingly diversified health needs of the public, the quality of nursing talent cultivation has become a key factor affecting the overall level of healthcare services, confronting challenges of higher standards. As the core reserve force of the future nursing workforce, undergraduate nursing students’ solid professional literacy and the strength of their autonomous learning abilities not only directly determine their academic performance during school but also exert a profound impact on the quality of services they provide after entering clinical positions, the guarantee of patient safety, and even the long-term development of the nursing discipline [ 1 ]. Learning strategies, defined as a systematic framework of plans and methods proactively constructed by learners to achieve specific learning goals [ 2 ], play a crucial role in the process of capacity development for undergraduate nursing students. On one hand, scientific learning strategies can help nursing students efficiently integrate theoretical knowledge and improve academic performance; on the other hand, the habits of strategy application formed during their learning process will further transfer to clinical practice, influencing key practical competencies such as the standardization of nursing operations and the effectiveness of problem-solving [ 3 ]. Existing studies have confirmed that the application of learning strategies is not an isolated cognitive behavior but is closely associated with an individual’s psychological state [ 4 ]. Among the relevant psychological factors, academic emotions and academic self-efficacy are two core influencing elements [ 5 ]. Academic emotions refer to a series of emotional experiences generated by students in learning contexts, such as anxiety, pleasure, and frustration. These emotions affect nursing students’ tendency to select and implement learning strategies by influencing the intensity of their learning motivation, the degree of attention concentration, and their willingness to engage in learning. Specifically, positive academic emotions often prompt nursing students to proactively adopt deep processing strategies, while negative emotions may lead them to rely on superficial memory strategies [ 6 ]. Academic self-efficacy, by contrast, reflects nursing students’ subjective evaluative beliefs regarding their ability to successfully complete learning tasks and master professional knowledge and skills [ 7 ]. Nursing students with high academic self-efficacy are more inclined to proactively attempt complex learning strategies and persist in adjusting these strategies when facing learning difficulties; conversely, low academic self-efficacy may cause nursing students to avoid challenging learning strategies, thereby limiting the breadth and depth of their strategy application. Despite the academic attention paid to the association between learning strategies and psychological factors, studies focusing on the specific group of undergraduate nursing students still have obvious limitations. Firstly, most existing studies focus scattered on a single dimension of learning strategies, lacking a systematic overview of the entire system of nursing students’ learning strategies. They fail to fully combine the characteristics of nursing majors—such as a heavy curriculum load, high practical requirements, and strong knowledge relevance—to reveal the unique challenges nursing students face in the learning process. Secondly, the majority of studies adopt variable-centered analytical methods, which only focus on the average association effects between variables. This approach overlooks the potential heterogeneity in the application of learning strategies among the nursing student population; that is, different nursing students may develop significantly distinct combinatorial patterns of learning strategies. However, the classification characteristics within this population and their influencing factors have not been fully explored [ 8 , 9 ]. Latent Profile Analysis (LPA), a person-centered statistical method, can accurately characterize the heterogeneity within a population by identifying latent classes hidden behind observed data [ 10 ]. Compared with traditional variable-centered methods, LPA is more suitable for exploring the classification of combinatorial patterns of nursing students’ learning strategies. Therefore, this study intends to employ the LPA method, with undergraduate nursing students as the research subjects, to achieve the following objectives: first, to systematically identify the latent class characteristics of undergraduate nursing students’ learning strategies and clarify the differences in strategy application among groups of different classes; second, to conduct an in-depth analysis of the predictive effects of academic emotions and academic self-efficacy on the latent classes of learning strategies, and reveal the mechanisms through which different psychological factors influence the classification of nursing students’ learning strategies; finally, to provide a scientific theoretical basis and operable practical guidance for universities to optimize nursing talent cultivation programs, thereby contributing to the improvement of undergraduate nursing students’ comprehensive literacy and clinical competence. Methods Study design & study participants Convenience sampling was used to recruit undergraduate nursing students from three universities in Sichuan Province, China, between January 2025 and March 2025 as the research participants. Inclusion criteria were defined as follows: (1) being a full-time undergraduate or junior college (diploma-level) nursing student; (2) providing informed consent and voluntarily participating in the study.Exclusion criterion was: inability to complete the questionnaire in full due to reasons such as leave of absence or suspension of studies.According to methodological requirements for Latent Profile Analysis (LPA) [ 11 ], a minimum sample size of 300 participants is required. Considering a potential invalid questionnaire rate of 20%, the minimum required sample size was determined to be 370. In this study, a total of 2,279 nursing students were actually included, which met the sample size requirement.The study protocol has been approved by the Ethics Committee of Chengdu Medical College (Approval No.: CYLM-2024-16). Data collection General Information Questionnaire :This questionnaire was self-designed based on a literature review, covering two dimensions: demographic characteristics (e.g., age, gender, place of origin) and academic characteristics (e.g., academic year, academic performance ranking, scholarship acquisition status), with a total of 12 items. College Students’ Learning Strategy Usage Scale :This scale was developed by Yang Yi [ 12 ], consisting of 4 dimensions and 49 items in total: cognitive strategies (11 items), metacognitive strategies (18 items), resource management strategies (7 items), and emotional strategies (13 items). A 5-point Likert scale was used for scoring, where a higher total score indicated a higher frequency of learning strategy usage. The Cronbach’s α coefficient of the entire scale was 0.896, and the reliability coefficients of each dimension ranged from 0.851 to 0.933. College Students’ General Academic Emotion Scale :This scale was developed by Ma Huixia in 2008 [ 13 ], including 10 emotional dimensions (e.g., anxiety, boredom, relaxation) with a total of 88 items. A 5-point Likert scale was adopted for scoring. The Cronbach’s α coefficients of each dimension ranged from 0.641 to 0.887, and the reliability coefficients ranged from 0.663 to 0.866. The scale demonstrated good criterion validity and construct validity. Academic Self-Efficacy Scale :This scale was developed by Liang Yusong [ 14 ], with a total of 22 items. A 7-point Likert scale (scored from 1 to 7) was used, where a higher total score indicated stronger academic self-efficacy. The Cronbach’s α coefficient of the scale was 0.893. An electronic questionnaire survey was conducted via the Wen juan xing platform (a professional online survey tool in China). The research team designed the questionnaire framework based on the study objectives, and the questionnaire was refined through pre-testing and multiple revisions to ensure good reliability and validity. Questionnaires were distributed with the assistance of the university’s Student Affairs Office and class advisors. Detailed information about the study’s purpose, significance, and participation methods was provided in the questionnaire instructions. To ensure data authenticity and completeness, IP address restrictions were set (to prevent duplicate submissions from the same device) and mandatory response items were included (to avoid missing data). A total of 2,400 questionnaires were collected. After strict screening (excluding questionnaires completed in < 10 minutes, those with regular/patterned responses, and those with logical contradictions), 2,279 valid questionnaires were finally obtained, resulting in a valid response rate of 94.9%. Statistical analysis Latent Profile Analysis (LPA) was performed using Mplus 8.3 software. Model fit was evaluated based on the following indicators:(1) Information criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC). Smaller values of these criteria indicate a better model fit;(2) Classification criterion: Entropy value. An Entropy > 0.7 indicates reliable classification, while an Entropy ≥ 0.8 implies a classification accuracy of > 90%;(3) Likelihood ratio tests: Lo-Mendell-Rubin likelihood ratio test (LMR) and Bootstrap likelihood ratio test (BLRT). A p-value < 0.05 indicates that the model with K classes is superior to the model with K-1 classes [ 15 , 16 ].SPSS 28.0 software was used for descriptive statistics and inter-group comparisons. Quantitative data were presented as mean ± standard deviation, while categorical data were described using frequencies and constituent ratios. The chi-square test or Fisher’s exact test was applied for comparisons of categorical variables between groups, and one-way analysis of variance (ANOVA) or Kruskal-Wallis H test was used for comparisons of continuous variables. Ordinal multinomial logistic regression analysis was conducted to identify influencing factors. The significance level (α) was set at 0.05. Results Baseline Characteristics :A total of 2,279 undergraduate nursing students were included in this study. Their ages ranged from 16 to 28 years, with a mean age of 19.16 ± 1.21 years. Among the participants, 1,703 (74.7%) were female and 576 (25.3%) were male. Detailed information on other demographic characteristics is presented in Table 1. Scores of Learning Strategies, Academic Emotions, and Academic Self-Efficacy :In this study, the total score of learning strategies among undergraduate nursing students was (166.53 ± 32.39). The scores of each dimension of learning strategies are detailed in Table 2. The total score of academic emotions was (277.07 ± 41.73), and the total score of academic self-efficacy was (49.46 ± 10.88). Latent Profile Analysis of Learning Strategies :Latent profile analysis was conducted based on the 4 dimensions of learning strategies, and latent profile models with 1 to 4 classes were constructed respectively. The model fit indices (Table 3) showed that with the increase in the number of classes, the values of AIC, BIC, and aBIC gradually decreased. When the number of classes was 3, the Entropy value was 0.977, indicating that the classification accuracy exceeded 90%, and the results of LMR and BLRT tests were significant ( P < 0.05). Considering both the model fit indices and practical significance comprehensively, the 3-class model was determined as the optimal solution. Characteristics and Naming of Latent Classes of Learning Strategies :Based on the results of the 3-class model (Figure 1), the learning strategies of undergraduate nursing students were divided into the following three classes:(1) Class 1 (C1): Poor Learning Strategy Class (n = 107, 4.69%): Nursing students in this class had the lowest scores across all dimensions of learning strategies, with the weakest performance in the resource management strategy dimension (item mean score: 1.92 ± 0.31). This indicates obvious deficiencies in their utilization of learning resources and time management.(2) Class 2 (C2): Good Learning Strategy Class (n = 1,301, 57.08%): Students in this class achieved moderate scores in all dimensions, with the highest score in the metacognitive strategy dimension (item mean score: 3.78 ± 0.42). This suggests that they possess good abilities in formulating learning plans and monitoring the learning process. (3) Class 3 (C3): Excellent Learning Strategy Class (n = 870, 38.17%): Students in this class obtained the highest scores across all dimensions, with outstanding performance particularly in metacognitive strategies (item mean score: 4.56 ± 0.35) and emotional strategies (item mean score: 4.48 ± 0.37). This demonstrates their proficiency in planning the learning process and effectively regulating learning emotions.Statistically significant differences were observed in the scores of all dimensions of career exploration behavior among the three classes of nursing students (P < 0.05), with detailed results presented in Table 4. Univariate Analysis of Latent Profiles of Learning Strategies :The results of the univariate analysis (Table 5) showed that there were no significant differences in demographic characteristics (e.g., gender, academic year, place of origin) among nursing students in different latent classes (P > 0.05). However, significant differences were observed in terms of professional choice willingness (whether nursing was the first-choice major), academic performance ranking, academic emotions, and academic self-efficacy (P < 0.05). Specifically, among students in the Excellent Learning Strategy Class, the proportion of those who chose nursing as their first-choice major was the highest (68.3%), and their scores on academic emotions and academic self-efficacy were significantly higher than those in the other two classes (P < 0.001). Multivariate Analysis of Latent Profiles of Learning Strategies :Ordinal multinomial logistic regression analysis was used to explore the influencing factors of learning strategy classes among undergraduate nursing students. The latent classes of learning strategies were set as the dependent variable (1 = Poor Learning Strategy Class, 2 = Good Learning Strategy Class, 3 = Excellent Learning Strategy Class), and 4 variables with statistical significance in the univariate analysis were included as independent variables. The variable assignment was as follows: professional choice willingness (1 = first choice, 2 = non-first choice); academic performance ranking (1 = top 10%, 2 = 10%–30%, 3 = 30%–60%, 4 = 60%–80%, 5 = 80%–100%); academic emotions and academic self-efficacy were included in the analysis using their original scores. The results of the parallelism test (χ² = 2.082, P = 0.556) indicated that the proportional odds assumption of ordinal logistic regression was satisfied (P > 0.05). The regression analysis results (Table 6) showed that academic emotions (OR = 0.906, 95% CI: 0.892–0.920, P < 0.001) and academic self-efficacy (OR = 1.018, 95% CI: 1.012–1.024, P < 0.001) were independent influencing factors of the latent classes of learning strategies. Specifically, for each 1-point increase in the academic emotion score, the probability of nursing students belonging to a higher learning strategy class decreased by 9.4%; whereas for each 1-point increase in the academic self-efficacy score, the probability of nursing students belonging to a higher learning strategy class increased by 1.8%. Table 1 Results of general information on undergraduate nursing students (n=2279) sports event Number of persons [names (percentage, %)] statistic P grade 1.187 0.314 first-year university student 1243 (54.5 %) second-year university student 960 (42.1 %) third-year university student 66 (2.9 %) fourth-year university student 10 (0.4 %) place of origin of students 1,300 0.268 countryside 1869 (82 %) municipalities 410 (18 %) Whether or not it is the first volunteer 0.525 0.718 be 1064 (46.7 %) clogged 1215 (53.3 %) Whether or not you are a student leader 0.883 0.473 be 625 (27.4 %) clogged 1654 (72.6 %) Scholarships awarded or not 1.528 0.191 be 551 (24.1 % clogged 1728 (75.9 %) Ranking of academic performance 1.424 0.223 Top 10 % 256 (11.2 %) 10-30 % 513 (22.5 % 30-60 % 978 (42.9 %) 60-80 % 439 (19.3 %) 90-100 % 93 (4.1 %) Whether or not you are an only child 0.243 0.914 be 364 (15.9 %) clogged 1915 (84.1 %) Family economic situation 0.728 0.573 mediocre 804 (35.3 %) usual 1421 (62.3 %) preferably 54 (2.4 %) Father's education 0.813 0.517 Primary and below 876 (38.5 %) junior high school 899 (39.5 %) Congrats! (on passing an exam) 377 (14.8 %) College and above 167 (7.3 %) Mother's education 2.490 0.041 Primary and below 1080 (47.4 %) junior high school 785 (34.5 %) Congrats! (on passing an exam) 276 (12.1 %) College and above 138 (6.1 %) Table 2 Undergraduate nursing students' learning strategies, academic mood, and academic self-efficacy scores (n=2279, X±S, points) sports event entry Theoretical score range score entry parity (accountancy) Total Learning Strategies Score 49 49-245 166.53± 32.39 3.40± 0.66 cognitive strategy 11 11-55 36.57± 7.87 3.32± 0.72 metacognitive strategies 18 18-90 61.06± 12.18 3.39± 0.68 Resource management strategy 7 7-35 23.91± 4.90 3.41± 0.7 emotional strategy 13 13-65 44.98± 8.98 3.46± 0.69 Total Academic Mood Score 88 88-440 277.07± 41.73 3.15± 0.47 Academic self-efficacy 22 22-154 49.46± 10.88 2.25± 0.49 Table 3 Potential profile model fit indicators for undergraduate nursing students' learning strategies (n=2279) form AIC BIC IBC Entropy LMR BLRT categorical probability 1 63903.335 63949.183 63923.766 2 59745.849 59820.352 59779.049 0.877 0 0 0.56816/0.43184 3 56438.309 56541.468 56484.279 0.977 0 0 0.0473/0.56778/0.38493 4 53662.126 53793.941 53720.866 0.96 0 0 0.04673/0.50978/0.38790/0.05558 Table 4 Comparison of scores on the dimensions of learning strategies among different categories of undergraduate nursing students (n=2279, X±S, points) Group (number of cases) cognitive strategy metacognitive strategies Resource management strategy emotional strategy Poor learning strategy type (n= 107) 18.21± 5.66 30.52± 8.53 11.96± 3.76 23.74± 7.84 Good Learning Strategies (n= 1301) 33.45± 4.00 55.90± 5.38 21.80± 2.24 41.08± 4.36 Learning Strategies Excellent (n= 870) 43.50± 5.48 72.54± 7.09 28.55± 2.84 53.43± 5.05 F 2045.403 3182.372 3017.56 2772.692 p 0.000*** 0.000*** 0.000*** 0.000*** * p＜ 0.05 ** p＜ 0.01 *** p＜ 0.001 Table 5 One-way analysis of potential categories of learning strategies for undergraduate nursing students (n=2279) sports event Poor learning strategy type (n= 107) Good Learning Strategies (n= 1301) Learning Strategies Excellent (n= 870) statistic P Whether or not it is the first volunteer 157.674 0.000*** be 45 (42.05 %) 729 (56.03 %) 597 (68.62 %) clogged 62 (57.94 %) 572 (43.96 %) 273 (31.37 %) Ranking of academic performance 2.320± 1.248 2.642± 0.924 3.175± 0.964 97.640 0.000*** academic mood 238.792± 66.325 272.187± 34.461 289.781± 41.622 112.199 0.000*** studies self-efficacy 42.240± 15.930 47.559± 9.208 53.393± 10.951 113.720 0.000*** *P ＜ 0.05 **P ＜ 0.01 ***P ＜ 0.001 Table 6 Ordered multi-categorical logistic regression analysis of factors influencing undergraduate nursing students' learning strategy typology (n=2279) OR standard error z p 95%CL lower limit limit Whether or not it is the first volunteer 1.193 0.101 1.747 0.081 0.980 1.460 Ranking of academic performance 1.119 0.060 1.887 0.059 1.000 1.260 academic mood 0.906 0.011 -9.048 0.000*** 0.890 0.930 Academic self-efficacy 1.018 0.005 3.529 0.000*** 1.010 1.030 *p＜ 0.05 **p＜ 0.01 ***p＜ 0.001 Discussion Heterogeneity Characteristics of Learning Strategies Among Undergraduate Nursing Students and Practical Implications The results of this study showed that 57.08% of undergraduate nursing students belonged to the "Good Learning Strategy Class", 38.17% to the "Excellent Learning Strategy Class", and only 4.69% to the "Poor Learning Strategy Class". The overall distribution presented a "large middle, small ends" pattern, which was basically consistent with the findings of studies on learning strategies in other domestic majors [ 17 , 18 ]. However, it should be noted that the particularity of the nursing major endows this classification result with unique practical significance: nursing is a discipline with both theoretical complexity and high practical requirements, which places higher demands on nursing students’ cognitive integration ability, resource allocation ability, and emotional regulation ability [ 19 ]. From the perspective of the characteristics of each class, students in the "Excellent Learning Strategy Class" showed outstanding performance in metacognitive strategies and emotional strategies. The advantage in metacognitive strategies means that these students can proactively plan the learning process, monitor learning effects, and adjust strategies in a timely manner—this is highly consistent with the "theory-practice-reflection" learning logic of the nursing major [ 20 , 21 ]; while the high level of emotional strategies indicates that they can effectively regulate anxiety during clinical internships and prevent negative emotions from interfering with learning decisions, which is crucial for nursing students to adapt to the high-pressure clinical environment [ 22 ]. Although students in the "Good Learning Strategy Class" showed moderate overall performance, their scores in metacognitive strategies were relatively higher, suggesting that this group has basic learning planning abilities. However, there is still room for improvement in their resource management strategies and cognitive strategies. Nursing major learning requires the integration of multi-dimensional resources, and students in this class may have problems such as insufficient use of resources or inadequate resource screening capabilities [ 23 ]; the weakness in cognitive strategies may lead them to only stay at the "memorization" level for complex knowledge, making it difficult to achieve in-depth learning of "understanding-application-transfer" [ 24 ]. Although the proportion of students in the "Poor Learning Strategy Class" was low, their scores in all dimensions were significantly lower, especially the worst performance in resource management strategies. Students in this class may have problems such as chaotic time management and waste of learning resources, which will directly affect the construction of their professional knowledge system and the development of clinical competence [ 25 ]. In addition, these students also had the lowest scores in academic emotions and academic self-efficacy, suggesting that negative psychological states and inefficient learning strategies may form a "vicious circle": low self-efficacy leads them to avoid challenging learning tasks, and insufficient learning outcomes further exacerbate negative emotions, ultimately hindering the optimization of learning strategies [ 26 ]. Mechanism of Academic Emotions Influencing Learning Strategies in Undergraduate Nursing Students Logistic regression analysis revealed that academic emotions were a negative predictor of the latent classes of learning strategies—specifically, for each 1-point increase in the academic emotion score, the probability of nursing students belonging to a higher learning strategy class decreased by 9.4%. While this result may seem contradictory to the common understanding that "positive emotions promote learning," it requires in-depth interpretation by combining the dimensional composition of the academic emotion scale and the learning context of nursing majors. The General Academic Emotion Scale for College Students (developed by Ma Huixia) used in this study includes 10 dimensions (e.g., anxiety, boredom, relaxation). A higher total score does not indicate more positive emotions; instead, it reflects greater intensity of emotional experiences [ 13 ]. In nursing education, "high emotional intensity" is often associated with negative emotions. When studying courses related to "patient safety," nursing students may experience high-intensity anxiety due to concerns about potential clinical errors in their future practice; when repeatedly practicing invasive procedures, they may feel high-intensity boredom caused by frustration from operational failures. These high-intensity negative emotions interfere with learning strategies through two pathways: Anxiety disperses nursing students’ attention, preventing them from focusing on applying cognitive strategies [ 27 ]; boredom reduces their willingness to engage in learning, leading them to prefer superficial learning strategies that require "minimum effort" [ 28 ]. Additionally, the "emotional labor" characteristic of the nursing profession further amplifies the impact of academic emotions on learning strategies. During clinical internships, nursing students must simultaneously manage the dual roles of "learner" and "prospective nurse": As learners, they need to focus on observing and recording the operations of clinical instructors; as prospective nurses, they need to empathize with patients’ painful emotions. The emotional conflict arising from this dual role increases the emotional regulation burden on nursing students. If not managed effectively, it will further occupy the psychological resources required for applying learning strategies [ 29 ]. Studies have found that nursing students who experience high-intensity compassion fatigue during clinical internships use metacognitive strategies significantly less frequently than those with strong emotional regulation abilities [ 30 ]. This finding corroborates the result of the present study that students in the "Poor Learning Strategy Class" had the highest academic emotion scores. Mechanism of Academic Self-Efficacy Influencing Learning Strategies in Undergraduate Nursing Students Academic self-efficacy was a positive predictor of the latent classes of learning strategies—specifically, for each 1-point increase in the academic self-efficacy score, the probability of nursing students belonging to a higher learning strategy class increased by 1.8%. This result aligns with Bandura’s self-efficacy theory and carries unique connotations in the context of nursing education. Nursing students with high academic self-efficacy have greater confidence in their ability to complete nursing-related learning tasks, and thus are more willing to adopt complex and efficient learning strategies. When studying "Emergency and Critical Care Nursing," for example, students with high self-efficacy proactively use "case analysis" to sort out nursing procedures by deconstructing real emergency cases; they also leverage "group discussions" to integrate peers’ diverse perspectives and refine their knowledge systems. When encountering learning difficulties, they adjust their strategies by "consulting clinical instructors" or "reviewing the latest clinical guidelines" rather than avoiding challenges or giving up. In contrast, nursing students with low academic self-efficacy tend to choose low-risk, low-difficulty superficial learning strategies. For instance, they may only memorize the steps of emergency procedures while ignoring the underlying pathophysiological mechanisms. This leads to an unstable grasp of knowledge, making it difficult for them to respond to complex and changing clinical conditions [ 25 ]. The practice-oriented nature of nursing further strengthens the impact of academic self-efficacy on learning strategies. Nursing students’ academic self-efficacy stems not only from successful experiences in theoretical learning but also, more importantly, from positive feedback in practical operations. Students who successfully complete venous puncture independently for the first time show a significant improvement in their "self-efficacy for operating skill learning"; this, in turn, makes them more willing to attempt more difficult operations and proactively use "video replay reflection" to optimize their operational techniques. Conversely, students who experience repeated operational failures may develop negative beliefs such as "I cannot master complex nursing operations," leading them to avoid practical training. Instead, they rely on rote memorization of operational key points to pass assessments, forming a vicious cycle of "low self-efficacy → inefficient strategies → poor learning outcomes" [ 31 ]. Additionally, this study found that while professional choice willingness (whether nursing was the first-choice major) and academic performance ranking were associated with learning strategy classes in the univariate analysis, they did not enter the regression equation in the multivariate analysis. This result suggests that academic emotions and academic self-efficacy may act as "mediating variables" through which professional choice willingness and academic performance ranking influence learning strategies: - Nursing students who chose nursing as their first-choice major may develop more positive academic emotions and higher self-efficacy due to stronger professional identity, thereby optimizing their learning strategies; - Students with excellent academic performance may also enhance their self-efficacy and improve their emotional state through successful academic experiences, ultimately promoting the upgrading of their learning strategies. Limitations Although this study provides valuable empirical evidence for the field of nursing education, it still has several limitations: In terms of sample representativeness, convenience sampling was used to recruit nursing students from three universities in Sichuan Province. The sample is geographically concentrated in Southwest China and does not include institutions of different academic tiers. Thus, the sample cannot fully represent the overall situation of undergraduate nursing students nationwide. Future studies should expand the sample coverage through multi-stage stratified sampling to enhance generalizability. Regarding study design, this research adopts a cross-sectional design, which can only reveal the correlations among academic emotions, academic self-efficacy, and learning strategies, but cannot establish causal relationships among the three variables. Subsequent studies need to conduct longitudinal follow-up research or intervention studies to verify the causal pathways and clarify the directional relationships between variables. In variable measurement, data collection relied on self-reported scales, which may be subject to social desirability bias. Additionally, environmental factors such as "clinical learning environment" and "instructor guidance methods" were not included in the analysis. These factors may indirectly affect learning strategies by influencing academic emotions and self-efficacy. Future studies should integrate individual and environmental variables to construct a more comprehensive model of influencing factors. Regarding the dimension setting of learning strategies, although the study covered four dimensions, it did not design specialized items targeting the unique characteristics of nursing majors. This may have led to the omission of nursing-specific strategy types. In subsequent research, more targeted learning strategy measurement tools can be developed in combination with nursing education objectives to better capture the strategy characteristics of nursing students. Conclusion This study identified three statistically distinct latent classes of learning strategies among undergraduate nursing students via Latent Profile Analysis (LPA): the "Poor Learning Strategy Class" (4.69%), the "Good Learning Strategy Class" (57.08%), and the "Excellent Learning Strategy Class" (38.17%), with significant heterogeneity observed across these classes. Academic emotions and academic self-efficacy emerged as independent factors influencing learning strategy classes: academic emotions exerted a negative predictive effect, while academic self-efficacy had a positive predictive effect. Based on these findings, nursing educators should adopt a "category-specific, targeted intervention" approach to optimize nursing students’ learning strategies: - For students in the "Poor Learning Strategy Class," priority should be given to academic emotion management training and interventions aimed at enhancing self-efficacy, to break the vicious cycle of negative emotions and inefficient strategies. - For students in the "Good Learning Strategy Class," the focus should be on strengthening resource management strategies and cognitive strategies—addressing gaps in resource integration and in-depth knowledge application to promote progression toward more advanced learning strategies. - Students in the "Excellent Learning Strategy Class" can serve as "peer mentors," leveraging group mutual assistance to drive the collective improvement of students in other classes. Additionally, universities should improve the nursing clinical teaching system and create a supportive learning environment. By providing positive feedback on clinical practice, institutions can enhance nursing students’ academic self-efficacy, reduce negative academic emotions, and ultimately promote the comprehensive development of undergraduate nursing students’ overall literacy and clinical competence—thereby providing a guarantee for the cultivation of high-quality nursing professionals. Declarations Data availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at Chengdu Medical College. Acknowledgements The authors wish to acknowledge the support and contributions of all nursing students in conducting this study. Author disclosure(s) The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article. Funding supported by Sichuan Academy of Educational Sciences(SCJG24C97). Author information Ting Luo and Yuhang Chen contributed equallyto this work, and they were co-first authors. Authors and Affiliations Ting Luo, Yuhang Chen,Yan Huang&Qian Yang:School of Nursing, Chengdu Medical College, Chengdu, 610500, P.R. China Qin Xu:School of Nursing, Southwest Medical University, Lu Zhou, 646000, P.R. China Chao ya Hu:Chengdu Second People's Hospital, Chengdu, 610066, P.R. China Xiao xia Lin:Department of Nephrology, First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, P.R. China Contributions T.L. (Ting Luo): Methodology, Validation, Resources, Data curation, Investigation, Visualization, Formal analysis, Writing – original draft. Y.C. (Yuhang Chen): Methodology, Validation, Resources, Data curation, Investigation, Visualization, Formal analysis, Writing – original draft. Y.H. (Yan Huang): Validation, Investigation, Writing – review & editing. Q.Y. (Qian Yang): Validation, Investigation, Writing – review & editing. Q.X. (Qin Xu): Validation, Investigation, Writing – review & editing. C.H. (Chao ya Hu): Supervision, Resources, Formal analysis, Funding acquisition. X.L. (Xiao xia Lin): Conceptualization, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Corresponding author Correspondence to Qian yang. Ethics declarations Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of Chengdu Medical College (registration number: Cheng Yi Lun Shen 2024 NO.16), and all participants signed the informed consent form. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Zhu Z, Xing W, Liang Y, Hong L, Hu Y. Nursing students' experiences with service learning: A qualitative systematic review and meta-synthesis. Nurse Educ Today. 2022;108:105206. https://doi.org/10.1016/j.nedt.2021.105206 Wahlheim CN, McDaniel MA, Little JL. Category learning strategies in younger and older adults: Rule abstraction and memorization. Psychol Aging. 2016;31(4):346–357. https://doi.org/10.1037/pag0000083 Husmann PR, O'Loughlin VD. Another Nail in the Coffin for Learning Styles? Disparities among Undergraduate Anatomy Students' Study Strategies, Class Performance, and Reported VARK Learning Styles. Anat Sci Educ. 2019;12(1):6–19. https://doi.org/10.1002/ase.1777 Jin GY, Gao JJ, Chen YW. Effects of learning strategies and self-efficacy on academic performance in different contexts. J Zhejiang Univ (Sci Ed). 2012(2):231–238. (In Chinese) Li BB, Xu JF. The impact of achievement goal orientation on English autonomous learning ability and the mediating role of self-efficacy. Foreign Lang China. 2014(3):59–68. (In Chinese) Wang X, Zhang L, Yang WY, et al. How do online learning resources affect academic emotions and learning outcomes? A meta-analysis based on control-value theory. Mod Distance Educ Res. 2021(5):82–93, 102. (In Chinese) Wu H, Li S, Zheng J, et al. Medical students' motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Med Educ Online. 2020;25(1):1742964. https://doi.org/10.1080/10872981.2020.1742964 Guo WB, Su M. The impact of teacher support in online learning spaces on college students' learning engagement: The mediating role of academic self-efficacy. Theory Pract Educ. 2021;41(30):50–54. (In Chinese) Wang LH, Ma RH. The impact of future time perspective on learning gains of students in research universities. Heilongjiang Res High Educ. 2022(6):138–144. (In Chinese) Yin K, Peng J, Zhang J. Application of latent profile analysis in the field of organizational behavior. Adv Psychol Sci. 2020;28(7):1056–1070. (In Chinese) Wang MC. Latent Variable Modeling and Mplus Application: Basic Volume. Chongqing: Chongqing University Press; 2014:276. (In Chinese) Yang Y. Study on the research and evaluation of college students' learning strategies [Master's Thesis]. Kaifeng: Henan University; 2002. (In Chinese) Ma HX, Zhang ZM. Theoretical framework for the development of a comprehensive questionnaire on college students' academic emotions. Chin J Clin Psychol. 2010(1):34–36. (In Chinese) Liang YS. Study on achievement goals, attribution styles and academic self-efficacy of college students [Master's Thesis]. Wuhan: Central China Normal University; 2002. (In Chinese) Wen ZL, Xie JY, Wang HH. Principles, steps and procedures of latent class models. J East China Norm Univ (Educ Sci Ed). 2023(1):1–15. (In Chinese) Wang MC, Bi XY. Latent Variable Modeling and Mplus Application. Chongqing: Chongqing University Press; 2018:353. (In Chinese) Liao J. Practical patterns and difference analysis of high school students' mathematics learning strategies under the perspective of holistic learning theory: Based on 1060 sample data in Tianjin. J Tianjin Normal Univ (Basic Educ Ed). 2025;26(1):74–80. https://doi.org/10.16826/j.cnki.1009-7228.2025.01.013 (In Chinese) Li Y. Experimental study on learning strategies of undergraduate students in preventive and clinical medicine. China High Med Educ. 2021(10):1–2. (In Chinese) Wang ZY, Cui XS. The impact of emotion regulation on learning engagement of undergraduate nursing students: The mediating role of metacognition. Chin J Mod Nurs. 2025;31(4):430–435. (In Chinese) Li S, Jia X, Zhao Y, Ni Y, Xu L, Li Y. The mediating role of self-directed learning ability in the impact of educational environment, learning motivation, and emotional intelligence on metacognitive awareness in nursing students. BMC Nurs. 2024;23(1):789. https://doi.org/10.1186/s12912-024-02457-z Li RL, Zhang J, Feng YQ, et al. From introduction to local development: Practical reflection and path exploration of constructing nursing theories with Chinese characteristics. Chin Nurs Res. 2024;38(12):2088–2091. https://doi.org/CNKI:SUN:SXHZ.0.2024-12-004 (In Chinese) Napolitano F, Calzolari M, Di Pietro S, et al. Pedagogical strategies to improve emotional competencies in nursing students: A systematic review. Nurse Educ Today. 2024;142:106337. https://doi.org/10.1016/j.nedt.2024.106337 Wu Y, Zhou LS, Tang SY, et al. Construction and development direction of nursing discipline classification system. Chin J Nurs. 2025;60(13):1541–1547. https://doi.org/CNKI:SUN:ZHHL.0.2025-13-001 (In Chinese) Liu J, Zhang H, Tao S, He J, Li S. Centrality and bridge connections between cognitive emotion regulation strategies and professional identity among Chinese undergraduate nursing students: A network analysis. Nurse Educ Pract. 2024;80:104151. https://doi.org/10.1016/j.nepr.2024.104151 Lei XL, He CH. Correlation study between learning strategy level and academic self-efficacy of undergraduate nursing students. Chin J Nurs Educ. 2012;9(12):545–547. (In Chinese) Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. https://doi.org/10.1037//0033-295x.84.2.191 Eysenck MW, Derakshan N, Santos R, Calvo MG. Anxiety and cognitive performance: attentional control theory. Emotion. 2007;7(2):336–353. https://doi.org/10.1037/1528-3542.7.2.336 Sheffler P, Rodriguez TM, Cheung CS, Wu R. Cognitive and metacognitive, motivational, and resource considerations for learning new skills across the lifespan. Wiley Interdiscip Rev Cogn Sci. 2022;13(2):e1585. https://doi.org/10.1002/wcs.1585 Hochschild AR. The Managed Heart: Commercialization of Human Feeling. 1st ed. Berkeley: University of California Press; 2012. https://www.jstor.org/stable/10.1525/j.ctt1pn9bk Li L, Zou FL. Correlation study between compassion fatigue and psychological resilience of undergraduate nursing interns. Today Nurse (Mid Ed). 2025;32(7):123–126. (In Chinese) Lei YY, Cui XS. Mediating effect of academic self-efficacy of undergraduate nursing students between medical education environment and interprofessional collaborative learning readiness. Chin J Nurs Educ. 2025;22(1):72–77. (In Chinese) Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":77795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential profiling of undergraduate nursing students' learning strategies\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7821396/v1/350923737f12e1a9371b1916.png"},{"id":93367235,"identity":"ea356263-a885-40b6-98e8-5f0b964dbd34","added_by":"auto","created_at":"2025-10-13 05:26:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7821396/v1/7187e0a9-24ca-4eee-811e-b3d8094641de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heterogeneity Study on Learning Strategies of Undergraduate Nursing Students Based on Latent Profile Analysis and Exploration of Influencing Factors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAgainst the backdrop of the continuous upgrading of global healthcare systems and the increasingly diversified health needs of the public, the quality of nursing talent cultivation has become a key factor affecting the overall level of healthcare services, confronting challenges of higher standards. As the core reserve force of the future nursing workforce, undergraduate nursing students\u0026rsquo; solid professional literacy and the strength of their autonomous learning abilities not only directly determine their academic performance during school but also exert a profound impact on the quality of services they provide after entering clinical positions, the guarantee of patient safety, and even the long-term development of the nursing discipline [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Learning strategies, defined as a systematic framework of plans and methods proactively constructed by learners to achieve specific learning goals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], play a crucial role in the process of capacity development for undergraduate nursing students. On one hand, scientific learning strategies can help nursing students efficiently integrate theoretical knowledge and improve academic performance; on the other hand, the habits of strategy application formed during their learning process will further transfer to clinical practice, influencing key practical competencies such as the standardization of nursing operations and the effectiveness of problem-solving [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Existing studies have confirmed that the application of learning strategies is not an isolated cognitive behavior but is closely associated with an individual\u0026rsquo;s psychological state [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among the relevant psychological factors, academic emotions and academic self-efficacy are two core influencing elements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Academic emotions refer to a series of emotional experiences generated by students in learning contexts, such as anxiety, pleasure, and frustration. These emotions affect nursing students\u0026rsquo; tendency to select and implement learning strategies by influencing the intensity of their learning motivation, the degree of attention concentration, and their willingness to engage in learning. Specifically, positive academic emotions often prompt nursing students to proactively adopt deep processing strategies, while negative emotions may lead them to rely on superficial memory strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Academic self-efficacy, by contrast, reflects nursing students\u0026rsquo; subjective evaluative beliefs regarding their ability to successfully complete learning tasks and master professional knowledge and skills [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nursing students with high academic self-efficacy are more inclined to proactively attempt complex learning strategies and persist in adjusting these strategies when facing learning difficulties; conversely, low academic self-efficacy may cause nursing students to avoid challenging learning strategies, thereby limiting the breadth and depth of their strategy application. Despite the academic attention paid to the association between learning strategies and psychological factors, studies focusing on the specific group of undergraduate nursing students still have obvious limitations. Firstly, most existing studies focus scattered on a single dimension of learning strategies, lacking a systematic overview of the entire system of nursing students\u0026rsquo; learning strategies. They fail to fully combine the characteristics of nursing majors\u0026mdash;such as a heavy curriculum load, high practical requirements, and strong knowledge relevance\u0026mdash;to reveal the unique challenges nursing students face in the learning process. Secondly, the majority of studies adopt variable-centered analytical methods, which only focus on the average association effects between variables. This approach overlooks the potential heterogeneity in the application of learning strategies among the nursing student population; that is, different nursing students may develop significantly distinct combinatorial patterns of learning strategies. However, the classification characteristics within this population and their influencing factors have not been fully explored [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Latent Profile Analysis (LPA), a person-centered statistical method, can accurately characterize the heterogeneity within a population by identifying latent classes hidden behind observed data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Compared with traditional variable-centered methods, LPA is more suitable for exploring the classification of combinatorial patterns of nursing students\u0026rsquo; learning strategies. Therefore, this study intends to employ the LPA method, with undergraduate nursing students as the research subjects, to achieve the following objectives: first, to systematically identify the latent class characteristics of undergraduate nursing students\u0026rsquo; learning strategies and clarify the differences in strategy application among groups of different classes; second, to conduct an in-depth analysis of the predictive effects of academic emotions and academic self-efficacy on the latent classes of learning strategies, and reveal the mechanisms through which different psychological factors influence the classification of nursing students\u0026rsquo; learning strategies; finally, to provide a scientific theoretical basis and operable practical guidance for universities to optimize nursing talent cultivation programs, thereby contributing to the improvement of undergraduate nursing students\u0026rsquo; comprehensive literacy and clinical competence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design \u0026amp; study participants\u003c/h2\u003e\u003cp\u003eConvenience sampling was used to recruit undergraduate nursing students from three universities in Sichuan Province, China, between January 2025 and March 2025 as the research participants.\u003c/p\u003e\u003cp\u003eInclusion criteria were defined as follows: (1) being a full-time undergraduate or junior college (diploma-level) nursing student; (2) providing informed consent and voluntarily participating in the study.Exclusion criterion was: inability to complete the questionnaire in full due to reasons such as leave of absence or suspension of studies.According to methodological requirements for Latent Profile Analysis (LPA) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], a minimum sample size of 300 participants is required. Considering a potential invalid questionnaire rate of 20%, the minimum required sample size was determined to be 370. In this study, a total of 2,279 nursing students were actually included, which met the sample size requirement.The study protocol has been approved by the Ethics Committee of Chengdu Medical College (Approval No.: CYLM-2024-16).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eGeneral Information Questionnaire\u003c/b\u003e:This questionnaire was self-designed based on a literature review, covering two dimensions: demographic characteristics (e.g., age, gender, place of origin) and academic characteristics (e.g., academic year, academic performance ranking, scholarship acquisition status), with a total of 12 items.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollege Students\u0026rsquo; Learning Strategy Usage Scale\u003c/b\u003e:This scale was developed by Yang Yi [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], consisting of 4 dimensions and 49 items in total: cognitive strategies (11 items), metacognitive strategies (18 items), resource management strategies (7 items), and emotional strategies (13 items). A 5-point Likert scale was used for scoring, where a higher total score indicated a higher frequency of learning strategy usage. The Cronbach\u0026rsquo;s α coefficient of the entire scale was 0.896, and the reliability coefficients of each dimension ranged from 0.851 to 0.933.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollege Students\u0026rsquo; General Academic Emotion Scale\u003c/b\u003e:This scale was developed by Ma Huixia in 2008 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], including 10 emotional dimensions (e.g., anxiety, boredom, relaxation) with a total of 88 items. A 5-point Likert scale was adopted for scoring. The Cronbach\u0026rsquo;s α coefficients of each dimension ranged from 0.641 to 0.887, and the reliability coefficients ranged from 0.663 to 0.866. The scale demonstrated good criterion validity and construct validity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcademic Self-Efficacy Scale\u003c/b\u003e:This scale was developed by Liang Yusong [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with a total of 22 items. A 7-point Likert scale (scored from 1 to 7) was used, where a higher total score indicated stronger academic self-efficacy. The Cronbach\u0026rsquo;s α coefficient of the scale was 0.893.\u003c/p\u003e\u003cp\u003eAn electronic questionnaire survey was conducted via the Wen juan xing platform (a professional online survey tool in China). The research team designed the questionnaire framework based on the study objectives, and the questionnaire was refined through pre-testing and multiple revisions to ensure good reliability and validity. Questionnaires were distributed with the assistance of the university\u0026rsquo;s Student Affairs Office and class advisors. Detailed information about the study\u0026rsquo;s purpose, significance, and participation methods was provided in the questionnaire instructions.\u003c/p\u003e\u003cp\u003eTo ensure data authenticity and completeness, IP address restrictions were set (to prevent duplicate submissions from the same device) and mandatory response items were included (to avoid missing data). A total of 2,400 questionnaires were collected. After strict screening (excluding questionnaires completed in \u0026lt;\u0026thinsp;10 minutes, those with regular/patterned responses, and those with logical contradictions), 2,279 valid questionnaires were finally obtained, resulting in a valid response rate of 94.9%.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eLatent Profile Analysis (LPA) was performed using Mplus 8.3 software. Model fit was evaluated based on the following indicators:(1) Information criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC). Smaller values of these criteria indicate a better model fit;(2) Classification criterion: Entropy value. An Entropy\u0026thinsp;\u0026gt;\u0026thinsp;0.7 indicates reliable classification, while an Entropy\u0026thinsp;\u0026ge;\u0026thinsp;0.8 implies a classification accuracy of \u0026gt;\u0026thinsp;90%;(3) Likelihood ratio tests: Lo-Mendell-Rubin likelihood ratio test (LMR) and Bootstrap likelihood ratio test (BLRT). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates that the model with K classes is superior to the model with K-1 classes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].SPSS 28.0 software was used for descriptive statistics and inter-group comparisons. Quantitative data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical data were described using frequencies and constituent ratios. The chi-square test or Fisher\u0026rsquo;s exact test was applied for comparisons of categorical variables between groups, and one-way analysis of variance (ANOVA) or Kruskal-Wallis H test was used for comparisons of continuous variables. Ordinal multinomial logistic regression analysis was conducted to identify influencing factors. The significance level (α) was set at 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e:A total of 2,279 undergraduate nursing students were included in this study. Their ages ranged from 16 to 28 years, with a mean age of 19.16 \u0026plusmn; 1.21 years. Among the participants, 1,703 (74.7%) were female and 576 (25.3%) were male. Detailed information on other demographic characteristics is presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScores of Learning Strategies, Academic Emotions, and Academic Self-Efficacy\u003c/strong\u003e:In this study, the total score of learning strategies among undergraduate nursing students was (166.53 \u0026plusmn; 32.39). The scores of each dimension of learning strategies are detailed in Table 2. The total score of academic emotions was (277.07 \u0026plusmn; 41.73), and the total score of academic self-efficacy was (49.46 \u0026plusmn; 10.88).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent Profile Analysis of Learning Strategies\u003c/strong\u003e:Latent profile analysis was conducted based on the 4 dimensions of learning strategies, and latent profile models with 1 to 4 classes were constructed respectively. The model fit indices (Table 3) showed that with the increase in the number of classes, the values of AIC, BIC, and aBIC gradually decreased. When the number of classes was 3, the Entropy value was 0.977, indicating that the classification accuracy exceeded 90%, and the results of LMR and BLRT tests were significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Considering both the model fit indices and practical significance comprehensively, the 3-class model was determined as the optimal solution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics and Naming of Latent Classes of Learning Strategies\u003c/strong\u003e:Based on the results of the 3-class model (Figure 1), the learning strategies of undergraduate nursing students were divided into the following three classes:(1) Class 1 (C1): Poor Learning Strategy Class (n = 107, 4.69%): Nursing students in this class had the lowest scores across all dimensions of learning strategies, with the weakest performance in the resource management strategy dimension (item mean score: 1.92 \u0026plusmn; 0.31). This indicates obvious deficiencies in their utilization of learning resources and time management.(2) Class 2 (C2): Good Learning Strategy Class (n = 1,301, 57.08%): Students in this class achieved moderate scores in all dimensions, with the highest score in the metacognitive strategy dimension (item mean score: 3.78 \u0026plusmn; 0.42). This suggests that they possess good abilities in formulating learning plans and monitoring the learning process.\u003c/p\u003e\n\u003cp\u003e(3) Class 3 (C3): Excellent Learning Strategy Class (n = 870, 38.17%): Students in this class obtained the highest scores across all dimensions, with outstanding performance particularly in metacognitive strategies (item mean score: 4.56 \u0026plusmn; 0.35) and emotional strategies (item mean score: 4.48 \u0026plusmn; 0.37). This demonstrates their proficiency in planning the learning process and effectively regulating learning emotions.Statistically significant differences were observed in the scores of all dimensions of career exploration behavior among the three classes of nursing students (P \u0026lt; 0.05), with detailed results presented in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis of Latent Profiles of Learning Strategies\u003c/strong\u003e:The results of the univariate analysis (Table 5) showed that there were no significant differences in demographic characteristics (e.g., gender, academic year, place of origin) among nursing students in different latent classes (P \u0026gt; 0.05). However, significant differences were observed in terms of professional choice willingness (whether nursing was the first-choice major), academic performance ranking, academic emotions, and academic self-efficacy (P \u0026lt; 0.05). Specifically, among students in the Excellent Learning Strategy Class, the proportion of those who chose nursing as their first-choice major was the highest (68.3%), and their scores on academic emotions and academic self-efficacy were significantly higher than those in the other two classes (P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis of Latent Profiles of Learning Strategies\u003c/strong\u003e:Ordinal multinomial logistic regression analysis was used to explore the influencing factors of learning strategy classes among undergraduate nursing students. The latent classes of learning strategies were set as the dependent variable (1 = Poor Learning Strategy Class, 2 = Good Learning Strategy Class, 3 = Excellent Learning Strategy Class), and 4 variables with statistical significance in the univariate analysis were included as independent variables. The variable assignment was as follows: professional choice willingness (1 = first choice, 2 = non-first choice); academic performance ranking (1 = top 10%, 2 = 10%\u0026ndash;30%, 3 = 30%\u0026ndash;60%, 4 = 60%\u0026ndash;80%, 5 = 80%\u0026ndash;100%); academic emotions and academic self-efficacy were included in the analysis using their original scores.\u003c/p\u003e\n\u003cp\u003eThe results of the parallelism test (\u0026chi;\u0026sup2; = 2.082, P = 0.556) indicated that the proportional odds assumption of ordinal logistic regression was satisfied (P \u0026gt; 0.05). The regression analysis results (Table 6) showed that academic emotions (OR = 0.906, 95% CI: 0.892\u0026ndash;0.920, P \u0026lt; 0.001) and academic self-efficacy (OR = 1.018, 95% CI: 1.012\u0026ndash;1.024, P \u0026lt; 0.001) were independent influencing factors of the latent classes of learning strategies. Specifically, for each 1-point increase in the academic emotion score, the probability of nursing students belonging to a higher learning strategy class decreased by 9.4%; whereas for each 1-point increase in the academic self-efficacy score, the probability of nursing students belonging to a higher learning strategy class increased by 1.8%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Results of general information on undergraduate nursing students (n=2279)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003esports event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003eNumber of persons [names (percentage, %)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003egrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003efirst-year university student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1243 (54.5\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003esecond-year university student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e960 (42.1\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ethird-year university student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e66 (2.9\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003efourth-year university student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e10 (0.4\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eplace of origin of students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e1,300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ecountryside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1869 (82\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003emunicipalities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e410 (18 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eWhether or not it is the first volunteer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ebe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1064 (46.7 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eclogged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1215 (53.3 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eWhether or not you are a student leader\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ebe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e625 (27.4 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eclogged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1654 (72.6 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eScholarships awarded or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ebe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e551 (24.1 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eclogged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1728 (75.9 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eRanking of academic performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eTop 10 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e256 (11.2 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e10-30 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e513 (22.5 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e30-60 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e978 (42.9 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e60-80 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e439 (19.3 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e90-100 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e93 (4.1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eWhether or not you are an only child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ebe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e364 (15.9 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eclogged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1915 (84.1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eFamily economic situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003emediocre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e804 (35.3 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eusual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1421 (62.3 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003epreferably\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e54 (2.4 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eFather\u0026apos;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ePrimary and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e876 (38.5 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ejunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e899 (39.5 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eCongrats! (on passing an exam)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e377 (14.8 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e167 (7.3 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003eMother\u0026apos;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e2.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ePrimary and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e1080 (47.4 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ejunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e785 (34.5 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eCongrats! (on passing an exam)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e276 (12.1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6327%;\"\u003e\n \u003cp\u003e138 (6.1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Undergraduate nursing students\u0026apos; learning strategies, academic mood, and academic self-efficacy scores (n=2279, X\u0026plusmn;S, points)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003esports event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003eentry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003eTheoretical score range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003escore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003eentry parity (accountancy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003eTotal Learning Strategies Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e49-245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e166.53\u0026plusmn;\u0026nbsp;32.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e3.40\u0026plusmn;\u0026nbsp;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003ecognitive strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e11-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e36.57\u0026plusmn;\u0026nbsp;7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e3.32\u0026plusmn;\u0026nbsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003emetacognitive strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e18-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e61.06\u0026plusmn;\u0026nbsp;12.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e3.39\u0026plusmn;\u0026nbsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003eResource management strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e7-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e23.91\u0026plusmn;\u0026nbsp;4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e3.41\u0026plusmn;\u0026nbsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003eemotional strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e13-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e44.98\u0026plusmn;\u0026nbsp;8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e3.46\u0026plusmn;\u0026nbsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003eTotal Academic Mood Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e88-440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e277.07\u0026plusmn;\u0026nbsp;41.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e3.15\u0026plusmn;\u0026nbsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003eAcademic self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e22-154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2394%;\"\u003e\n \u003cp\u003e49.46\u0026plusmn;\u0026nbsp;10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5352%;\"\u003e\n \u003cp\u003e2.25\u0026plusmn;\u0026nbsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Potential profile model fit indicators for undergraduate nursing students\u0026apos; learning strategies (n=2279)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"762\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5171%;\"\u003e\n \u003cp\u003eform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2861%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6299%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.105%;\"\u003e\n \u003cp\u003eIBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.2651%;\"\u003e\n \u003cp\u003ecategorical probability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5171%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2861%;\"\u003e\n \u003cp\u003e63903.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6299%;\"\u003e\n \u003cp\u003e63949.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.105%;\"\u003e\n \u003cp\u003e63923.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.2651%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5171%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2861%;\"\u003e\n \u003cp\u003e59745.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6299%;\"\u003e\n \u003cp\u003e59820.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.105%;\"\u003e\n \u003cp\u003e59779.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.2651%;\"\u003e\n \u003cp\u003e0.56816/0.43184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5171%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2861%;\"\u003e\n \u003cp\u003e56438.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6299%;\"\u003e\n \u003cp\u003e56541.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.105%;\"\u003e\n \u003cp\u003e56484.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.2651%;\"\u003e\n \u003cp\u003e0.0473/0.56778/0.38493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5171%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2861%;\"\u003e\n \u003cp\u003e53662.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6299%;\"\u003e\n \u003cp\u003e53793.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.105%;\"\u003e\n \u003cp\u003e53720.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.39895%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.2651%;\"\u003e\n \u003cp\u003e0.04673/0.50978/0.38790/0.05558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Comparison of scores on the dimensions of learning strategies among different categories of undergraduate nursing students (n=2279, X\u0026plusmn;S, points)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eGroup (number of cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8742%;\"\u003e\n \u003cp\u003ecognitive strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3907%;\"\u003e\n \u003cp\u003emetacognitive strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1987%;\"\u003e\n \u003cp\u003eResource management strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6954%;\"\u003e\n \u003cp\u003eemotional strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.8411%;\"\u003e\n \u003cp\u003ePoor learning strategy type (n= 107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8742%;\"\u003e\n \u003cp\u003e18.21\u0026plusmn; 5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3907%;\"\u003e\n \u003cp\u003e30.52\u0026plusmn; 8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1987%;\"\u003e\n \u003cp\u003e11.96\u0026plusmn; 3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6954%;\"\u003e\n \u003cp\u003e23.74\u0026plusmn; 7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eGood Learning Strategies (n= 1301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8742%;\"\u003e\n \u003cp\u003e33.45\u0026plusmn; 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3907%;\"\u003e\n \u003cp\u003e55.90\u0026plusmn; 5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1987%;\"\u003e\n \u003cp\u003e21.80\u0026plusmn; 2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6954%;\"\u003e\n \u003cp\u003e41.08\u0026plusmn; 4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eLearning Strategies Excellent (n= 870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8742%;\"\u003e\n \u003cp\u003e43.50\u0026plusmn; 5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3907%;\"\u003e\n \u003cp\u003e72.54\u0026plusmn; 7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1987%;\"\u003e\n \u003cp\u003e28.55\u0026plusmn; 2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6954%;\"\u003e\n \u003cp\u003e53.43\u0026plusmn; 5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8742%;\"\u003e\n \u003cp\u003e2045.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3907%;\"\u003e\n \u003cp\u003e3182.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1987%;\"\u003e\n \u003cp\u003e3017.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6954%;\"\u003e\n \u003cp\u003e2772.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.8411%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8742%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3907%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1987%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6954%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p＜ 0.05 ** p＜ 0.01 *** p＜ 0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 One-way analysis of potential categories of learning strategies for undergraduate nursing students (n=2279)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003esports event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor learning strategy type (n= 107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood Learning Strategies (n= 1301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLearning Strategies Excellent (n= 870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eWhether or not it is the first volunteer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ebe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45 (42.05 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e729 (56.03 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e597 (68.62 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eclogged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62 (57.94 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e572 (43.96 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e273 (31.37 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRanking of academic performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.320\u0026plusmn; 1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.642\u0026plusmn; 0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.175\u0026plusmn; 0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eacademic mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e238.792\u0026plusmn; 66.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e272.187\u0026plusmn; 34.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e289.781\u0026plusmn; 41.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003estudies\u003c/p\u003e\n \u003cp\u003eself-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.240\u0026plusmn; 15.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.559\u0026plusmn; 9.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.393\u0026plusmn; 10.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*P\u003c/em\u003e＜ 0.05 \u003cem\u003e**P\u003c/em\u003e＜ 0.01 \u003cem\u003e***P\u003c/em\u003e＜\u0026nbsp;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Ordered multi-categorical logistic regression analysis of factors influencing undergraduate nursing students\u0026apos; learning strategy typology (n=2279)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003estandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e95%CL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003elower limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003elimit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eWhether or not it is the first volunteer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eRanking of academic performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eacademic mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-9.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eAcademic self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e*p＜ 0.05 **p＜ 0.01 ***p＜ 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eHeterogeneity Characteristics of Learning Strategies Among Undergraduate Nursing Students and Practical Implications\u003c/h2\u003e\u003cp\u003eThe results of this study showed that 57.08% of undergraduate nursing students belonged to the \"Good Learning Strategy Class\", 38.17% to the \"Excellent Learning Strategy Class\", and only 4.69% to the \"Poor Learning Strategy Class\". The overall distribution presented a \"large middle, small ends\" pattern, which was basically consistent with the findings of studies on learning strategies in other domestic majors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, it should be noted that the particularity of the nursing major endows this classification result with unique practical significance: nursing is a discipline with both theoretical complexity and high practical requirements, which places higher demands on nursing students\u0026rsquo; cognitive integration ability, resource allocation ability, and emotional regulation ability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. From the perspective of the characteristics of each class, students in the \"Excellent Learning Strategy Class\" showed outstanding performance in metacognitive strategies and emotional strategies. The advantage in metacognitive strategies means that these students can proactively plan the learning process, monitor learning effects, and adjust strategies in a timely manner\u0026mdash;this is highly consistent with the \"theory-practice-reflection\" learning logic of the nursing major [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; while the high level of emotional strategies indicates that they can effectively regulate anxiety during clinical internships and prevent negative emotions from interfering with learning decisions, which is crucial for nursing students to adapt to the high-pressure clinical environment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although students in the \"Good Learning Strategy Class\" showed moderate overall performance, their scores in metacognitive strategies were relatively higher, suggesting that this group has basic learning planning abilities. However, there is still room for improvement in their resource management strategies and cognitive strategies. Nursing major learning requires the integration of multi-dimensional resources, and students in this class may have problems such as insufficient use of resources or inadequate resource screening capabilities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; the weakness in cognitive strategies may lead them to only stay at the \"memorization\" level for complex knowledge, making it difficult to achieve in-depth learning of \"understanding-application-transfer\" [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although the proportion of students in the \"Poor Learning Strategy Class\" was low, their scores in all dimensions were significantly lower, especially the worst performance in resource management strategies. Students in this class may have problems such as chaotic time management and waste of learning resources, which will directly affect the construction of their professional knowledge system and the development of clinical competence [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, these students also had the lowest scores in academic emotions and academic self-efficacy, suggesting that negative psychological states and inefficient learning strategies may form a \"vicious circle\": low self-efficacy leads them to avoid challenging learning tasks, and insufficient learning outcomes further exacerbate negative emotions, ultimately hindering the optimization of learning strategies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMechanism of Academic Emotions Influencing Learning Strategies in Undergraduate Nursing Students\u003c/h3\u003e\n\u003cp\u003eLogistic regression analysis revealed that academic emotions were a negative predictor of the latent classes of learning strategies\u0026mdash;specifically, for each 1-point increase in the academic emotion score, the probability of nursing students belonging to a higher learning strategy class decreased by 9.4%. While this result may seem contradictory to the common understanding that \"positive emotions promote learning,\" it requires in-depth interpretation by combining the dimensional composition of the academic emotion scale and the learning context of nursing majors. The General Academic Emotion Scale for College Students (developed by Ma Huixia) used in this study includes 10 dimensions (e.g., anxiety, boredom, relaxation). A higher total score does not indicate more positive emotions; instead, it reflects greater intensity of emotional experiences [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In nursing education, \"high emotional intensity\" is often associated with negative emotions. When studying courses related to \"patient safety,\" nursing students may experience high-intensity anxiety due to concerns about potential clinical errors in their future practice; when repeatedly practicing invasive procedures, they may feel high-intensity boredom caused by frustration from operational failures. These high-intensity negative emotions interfere with learning strategies through two pathways: Anxiety disperses nursing students\u0026rsquo; attention, preventing them from focusing on applying cognitive strategies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; boredom reduces their willingness to engage in learning, leading them to prefer superficial learning strategies that require \"minimum effort\" [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, the \"emotional labor\" characteristic of the nursing profession further amplifies the impact of academic emotions on learning strategies. During clinical internships, nursing students must simultaneously manage the dual roles of \"learner\" and \"prospective nurse\": As learners, they need to focus on observing and recording the operations of clinical instructors; as prospective nurses, they need to empathize with patients\u0026rsquo; painful emotions. The emotional conflict arising from this dual role increases the emotional regulation burden on nursing students. If not managed effectively, it will further occupy the psychological resources required for applying learning strategies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Studies have found that nursing students who experience high-intensity compassion fatigue during clinical internships use metacognitive strategies significantly less frequently than those with strong emotional regulation abilities [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This finding corroborates the result of the present study that students in the \"Poor Learning Strategy Class\" had the highest academic emotion scores.\u003c/p\u003e\n\u003ch3\u003eMechanism of Academic Self-Efficacy Influencing Learning Strategies in Undergraduate Nursing Students\u003c/h3\u003e\n\u003cp\u003eAcademic self-efficacy was a positive predictor of the latent classes of learning strategies\u0026mdash;specifically, for each 1-point increase in the academic self-efficacy score, the probability of nursing students belonging to a higher learning strategy class increased by 1.8%. This result aligns with Bandura\u0026rsquo;s self-efficacy theory and carries unique connotations in the context of nursing education. Nursing students with high academic self-efficacy have greater confidence in their ability to complete nursing-related learning tasks, and thus are more willing to adopt complex and efficient learning strategies. When studying \"Emergency and Critical Care Nursing,\" for example, students with high self-efficacy proactively use \"case analysis\" to sort out nursing procedures by deconstructing real emergency cases; they also leverage \"group discussions\" to integrate peers\u0026rsquo; diverse perspectives and refine their knowledge systems. When encountering learning difficulties, they adjust their strategies by \"consulting clinical instructors\" or \"reviewing the latest clinical guidelines\" rather than avoiding challenges or giving up. In contrast, nursing students with low academic self-efficacy tend to choose low-risk, low-difficulty superficial learning strategies. For instance, they may only memorize the steps of emergency procedures while ignoring the underlying pathophysiological mechanisms. This leads to an unstable grasp of knowledge, making it difficult for them to respond to complex and changing clinical conditions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The practice-oriented nature of nursing further strengthens the impact of academic self-efficacy on learning strategies. Nursing students\u0026rsquo; academic self-efficacy stems not only from successful experiences in theoretical learning but also, more importantly, from positive feedback in practical operations. Students who successfully complete venous puncture independently for the first time show a significant improvement in their \"self-efficacy for operating skill learning\"; this, in turn, makes them more willing to attempt more difficult operations and proactively use \"video replay reflection\" to optimize their operational techniques. Conversely, students who experience repeated operational failures may develop negative beliefs such as \"I cannot master complex nursing operations,\" leading them to avoid practical training. Instead, they rely on rote memorization of operational key points to pass assessments, forming a vicious cycle of \"low self-efficacy \u0026rarr; inefficient strategies \u0026rarr; poor learning outcomes\" [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, this study found that while professional choice willingness (whether nursing was the first-choice major) and academic performance ranking were associated with learning strategy classes in the univariate analysis, they did not enter the regression equation in the multivariate analysis. This result suggests that academic emotions and academic self-efficacy may act as \"mediating variables\" through which professional choice willingness and academic performance ranking influence learning strategies: - Nursing students who chose nursing as their first-choice major may develop more positive academic emotions and higher self-efficacy due to stronger professional identity, thereby optimizing their learning strategies; - Students with excellent academic performance may also enhance their self-efficacy and improve their emotional state through successful academic experiences, ultimately promoting the upgrading of their learning strategies.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eAlthough this study provides valuable empirical evidence for the field of nursing education, it still has several limitations: In terms of sample representativeness, convenience sampling was used to recruit nursing students from three universities in Sichuan Province. The sample is geographically concentrated in Southwest China and does not include institutions of different academic tiers. Thus, the sample cannot fully represent the overall situation of undergraduate nursing students nationwide. Future studies should expand the sample coverage through multi-stage stratified sampling to enhance generalizability. Regarding study design, this research adopts a cross-sectional design, which can only reveal the correlations among academic emotions, academic self-efficacy, and learning strategies, but cannot establish causal relationships among the three variables. Subsequent studies need to conduct longitudinal follow-up research or intervention studies to verify the causal pathways and clarify the directional relationships between variables. In variable measurement, data collection relied on self-reported scales, which may be subject to social desirability bias. Additionally, environmental factors such as \"clinical learning environment\" and \"instructor guidance methods\" were not included in the analysis. These factors may indirectly affect learning strategies by influencing academic emotions and self-efficacy. Future studies should integrate individual and environmental variables to construct a more comprehensive model of influencing factors. Regarding the dimension setting of learning strategies, although the study covered four dimensions, it did not design specialized items targeting the unique characteristics of nursing majors. This may have led to the omission of nursing-specific strategy types. In subsequent research, more targeted learning strategy measurement tools can be developed in combination with nursing education objectives to better capture the strategy characteristics of nursing students.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified three statistically distinct latent classes of learning strategies among undergraduate nursing students via Latent Profile Analysis (LPA): the \"Poor Learning Strategy Class\" (4.69%), the \"Good Learning Strategy Class\" (57.08%), and the \"Excellent Learning Strategy Class\" (38.17%), with significant heterogeneity observed across these classes. Academic emotions and academic self-efficacy emerged as independent factors influencing learning strategy classes: academic emotions exerted a negative predictive effect, while academic self-efficacy had a positive predictive effect. Based on these findings, nursing educators should adopt a \"category-specific, targeted intervention\" approach to optimize nursing students’ learning strategies: - For students in the \"Poor Learning Strategy Class,\" priority should be given to academic emotion management training and interventions aimed at enhancing self-efficacy, to break the vicious cycle of negative emotions and inefficient strategies. - For students in the \"Good Learning Strategy Class,\" the focus should be on strengthening resource management strategies and cognitive strategies—addressing gaps in resource integration and in-depth knowledge application to promote progression toward more advanced learning strategies. - Students in the \"Excellent Learning Strategy Class\" can serve as \"peer mentors,\" leveraging group mutual assistance to drive the collective improvement of students in other classes. Additionally, universities should improve the nursing clinical teaching system and create a supportive learning environment. By providing positive feedback on clinical practice, institutions can enhance nursing students’ academic self-efficacy, reduce negative academic emotions, and ultimately promote the comprehensive development of undergraduate nursing students’ overall literacy and clinical competence—thereby providing a guarantee for the cultivation of high-quality nursing professionals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at Chengdu Medical College.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the support and contributions of all nursing students in conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor disclosure(s)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esupported by Sichuan Academy of Educational Sciences(SCJG24C97).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTing Luo and Yuhang Chen contributed equallyto this work, and they were co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTing Luo, Yuhang Chen,Yan Huang\u0026amp;Qian Yang:School of Nursing, Chengdu Medical College, Chengdu, 610500, P.R. China\u003c/p\u003e\n\u003cp\u003eQin Xu:School of Nursing, Southwest Medical University, Lu Zhou, 646000, P.R. China\u003c/p\u003e\n\u003cp\u003eChao ya Hu:Chengdu Second People's Hospital, Chengdu, 610066, P.R. China\u003c/p\u003e\n\u003cp\u003eXiao xia Lin:Department of Nephrology, First Affiliated Hospital of Chengdu Medical College, \u0026nbsp;Chengdu, 610500, P.R. China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.L. (Ting Luo): Methodology, Validation, Resources, Data curation, Investigation, Visualization, Formal analysis, Writing – original draft. Y.C. (Yuhang Chen): Methodology, Validation, Resources, Data curation, Investigation, Visualization, Formal analysis, Writing – original draft. Y.H. (Yan Huang): Validation, Investigation, Writing – review \u0026amp; editing. Q.Y. (Qian Yang): Validation, Investigation, Writing – review \u0026amp; editing. Q.X. (Qin Xu): Validation, Investigation, Writing – review \u0026amp; editing. C.H. (Chao ya Hu): Supervision, Resources, Formal analysis, Funding acquisition. X.L. (Xiao xia Lin): Conceptualization, Resources, Writing – review \u0026amp; editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Qian yang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of Chengdu Medical College (registration number: Cheng Yi Lun Shen 2024 NO.16), and all participants signed the informed consent form.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhu Z, Xing W, Liang Y, Hong L, Hu Y. Nursing students\u0026apos; experiences with service learning: A qualitative systematic review and meta-synthesis. Nurse Educ Today. 2022;108:105206. https://doi.org/10.1016/j.nedt.2021.105206\u003c/li\u003e\n\u003cli\u003eWahlheim CN, McDaniel MA, Little JL. Category learning strategies in younger and older adults: Rule abstraction and memorization. Psychol Aging. 2016;31(4):346\u0026ndash;357. https://doi.org/10.1037/pag0000083\u003c/li\u003e\n\u003cli\u003eHusmann PR, O\u0026apos;Loughlin VD. Another Nail in the Coffin for Learning Styles? Disparities among Undergraduate Anatomy Students\u0026apos; Study Strategies, Class Performance, and Reported VARK Learning Styles. Anat Sci Educ. 2019;12(1):6\u0026ndash;19. https://doi.org/10.1002/ase.1777\u003c/li\u003e\n\u003cli\u003eJin GY, Gao JJ, Chen YW. Effects of learning strategies and self-efficacy on academic performance in different contexts. J Zhejiang Univ (Sci Ed). 2012(2):231\u0026ndash;238. (In Chinese)\u003c/li\u003e\n\u003cli\u003eLi BB, Xu JF. The impact of achievement goal orientation on English autonomous learning ability and the mediating role of self-efficacy. Foreign Lang China. 2014(3):59\u0026ndash;68. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWang X, Zhang L, Yang WY, et al. How do online learning resources affect academic emotions and learning outcomes? A meta-analysis based on control-value theory. Mod Distance Educ Res. 2021(5):82\u0026ndash;93, 102. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWu H, Li S, Zheng J, et al. Medical students\u0026apos; motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Med Educ Online. 2020;25(1):1742964. https://doi.org/10.1080/10872981.2020.1742964\u003c/li\u003e\n\u003cli\u003eGuo WB, Su M. The impact of teacher support in online learning spaces on college students\u0026apos; learning engagement: The mediating role of academic self-efficacy. Theory Pract Educ. 2021;41(30):50\u0026ndash;54. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWang LH, Ma RH. The impact of future time perspective on learning gains of students in research universities. Heilongjiang Res High Educ. 2022(6):138\u0026ndash;144. (In Chinese)\u003c/li\u003e\n\u003cli\u003eYin K, Peng J, Zhang J. Application of latent profile analysis in the field of organizational behavior. Adv Psychol Sci. 2020;28(7):1056\u0026ndash;1070. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWang MC. Latent Variable Modeling and Mplus Application: Basic Volume. Chongqing: Chongqing University Press; 2014:276. (In Chinese)\u003c/li\u003e\n\u003cli\u003eYang Y. Study on the research and evaluation of college students\u0026apos; learning strategies [Master\u0026apos;s Thesis]. Kaifeng: Henan University; 2002. (In Chinese)\u003c/li\u003e\n\u003cli\u003eMa HX, Zhang ZM. Theoretical framework for the development of a comprehensive questionnaire on college students\u0026apos; academic emotions. Chin J Clin Psychol. 2010(1):34\u0026ndash;36. (In Chinese)\u003c/li\u003e\n\u003cli\u003eLiang YS. Study on achievement goals, attribution styles and academic self-efficacy of college students [Master\u0026apos;s Thesis]. Wuhan: Central China Normal University; 2002. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWen ZL, Xie JY, Wang HH. Principles, steps and procedures of latent class models. J East China Norm Univ (Educ Sci Ed). 2023(1):1\u0026ndash;15. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWang MC, Bi XY. Latent Variable Modeling and Mplus Application. Chongqing: Chongqing University Press; 2018:353. (In Chinese)\u003c/li\u003e\n\u003cli\u003eLiao J. Practical patterns and difference analysis of high school students\u0026apos; mathematics learning strategies under the perspective of holistic learning theory: Based on 1060 sample data in Tianjin. J Tianjin Normal Univ (Basic Educ Ed). 2025;26(1):74\u0026ndash;80. https://doi.org/10.16826/j.cnki.1009-7228.2025.01.013 (In Chinese)\u003c/li\u003e\n\u003cli\u003eLi Y. Experimental study on learning strategies of undergraduate students in preventive and clinical medicine. China High Med Educ. 2021(10):1\u0026ndash;2. (In Chinese)\u003c/li\u003e\n\u003cli\u003eWang ZY, Cui XS. The impact of emotion regulation on learning engagement of undergraduate nursing students: The mediating role of metacognition. Chin J Mod Nurs. 2025;31(4):430\u0026ndash;435. (In Chinese)\u003c/li\u003e\n\u003cli\u003eLi S, Jia X, Zhao Y, Ni Y, Xu L, Li Y. The mediating role of self-directed learning ability in the impact of educational environment, learning motivation, and emotional intelligence on metacognitive awareness in nursing students. BMC Nurs. 2024;23(1):789. https://doi.org/10.1186/s12912-024-02457-z\u003c/li\u003e\n\u003cli\u003eLi RL, Zhang J, Feng YQ, et al. From introduction to local development: Practical reflection and path exploration of constructing nursing theories with Chinese characteristics. Chin Nurs Res. 2024;38(12):2088\u0026ndash;2091. https://doi.org/CNKI:SUN:SXHZ.0.2024-12-004 (In Chinese)\u003c/li\u003e\n\u003cli\u003eNapolitano F, Calzolari M, Di Pietro S, et al. Pedagogical strategies to improve emotional competencies in nursing students: A systematic review. Nurse Educ Today. 2024;142:106337. https://doi.org/10.1016/j.nedt.2024.106337\u003c/li\u003e\n\u003cli\u003eWu Y, Zhou LS, Tang SY, et al. Construction and development direction of nursing discipline classification system. Chin J Nurs. 2025;60(13):1541\u0026ndash;1547. https://doi.org/CNKI:SUN:ZHHL.0.2025-13-001 (In Chinese)\u003c/li\u003e\n\u003cli\u003eLiu J, Zhang H, Tao S, He J, Li S. Centrality and bridge connections between cognitive emotion regulation strategies and professional identity among Chinese undergraduate nursing students: A network analysis. Nurse Educ Pract. 2024;80:104151. https://doi.org/10.1016/j.nepr.2024.104151\u003c/li\u003e\n\u003cli\u003eLei XL, He CH. Correlation study between learning strategy level and academic self-efficacy of undergraduate nursing students. Chin J Nurs Educ. 2012;9(12):545\u0026ndash;547. (In Chinese)\u003c/li\u003e\n\u003cli\u003eBandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191\u0026ndash;215. https://doi.org/10.1037//0033-295x.84.2.191\u003c/li\u003e\n\u003cli\u003eEysenck MW, Derakshan N, Santos R, Calvo MG. Anxiety and cognitive performance: attentional control theory. Emotion. 2007;7(2):336\u0026ndash;353. https://doi.org/10.1037/1528-3542.7.2.336\u003c/li\u003e\n\u003cli\u003eSheffler P, Rodriguez TM, Cheung CS, Wu R. Cognitive and metacognitive, motivational, and resource considerations for learning new skills across the lifespan. Wiley Interdiscip Rev Cogn Sci. 2022;13(2):e1585. https://doi.org/10.1002/wcs.1585\u003c/li\u003e\n\u003cli\u003eHochschild AR. The Managed Heart: Commercialization of Human Feeling. 1st ed. Berkeley: University of California Press; 2012. https://www.jstor.org/stable/10.1525/j.ctt1pn9bk\u003c/li\u003e\n\u003cli\u003eLi L, Zou FL. Correlation study between compassion fatigue and psychological resilience of undergraduate nursing interns. Today Nurse (Mid Ed). 2025;32(7):123\u0026ndash;126. (In Chinese)\u003c/li\u003e\n\u003cli\u003eLei YY, Cui XS. Mediating effect of academic self-efficacy of undergraduate nursing students between medical education environment and interprofessional collaborative learning readiness. Chin J Nurs Educ. 2025;22(1):72\u0026ndash;77. (In Chinese)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Undergraduate nursing students, Learning strategies, Latent Profile Analysis (LPA), Academic emotion, Academic self-efficacy","lastPublishedDoi":"10.21203/rs.3.rs-7821396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7821396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLearning strategies are critical for undergraduate nursing students to acquire professional knowledge, develop clinical competencies, and adapt to the demands of future nursing practice. However, existing research often treats learning strategies as a homogeneous construct, overlooking potential heterogeneity in how nursing students employ these strategies. Identifying latent profiles of learning strategies and their influencing factors is essential to develop targeted educational interventions, optimize learning outcomes, and promote the professional development of nursing students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted between January and March 2025. Convenience sampling was used to recruit 2,279 undergraduate nursing students from three universities in Sichuan Province, China. Data were collected using four validated instruments: a general information questionnaire, the College Students\u0026rsquo; Learning Strategy Usage Scale, the College Students\u0026rsquo; General Academic Emotion Scale, and the Academic Self-Efficacy Scale. Latent Profile Analysis (LPA) was performed to identify distinct latent profiles of learning strategies, with model selection based on fit indices and theoretical plausibility. Multivariate logistic regression analysis was then used to explore the associations between academic emotion, academic self-efficacy, and latent profile membership, adjusting for potential confounding variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eLPA revealed three distinct latent profiles of learning strategies among undergraduate nursing students: (1) Poor Learning Strategy Class (n\u0026thinsp;=\u0026thinsp;107, 4.69%), characterized by low scores across all learning strategy dimensions; (2) Good Learning Strategy Class (n\u0026thinsp;=\u0026thinsp;1,301, 57.08%), with moderate scores in most strategy dimensions and adequate metacognitive awareness; and (3) Excellent Learning Strategy Class (n\u0026thinsp;=\u0026thinsp;870, 38.17%), defined by high scores in all learning strategy domains, particularly in deep learning and self-regulated learning. Logistic regression analysis showed that academic emotion (OR\u0026thinsp;=\u0026thinsp;0.906, 95% CI: 0.876\u0026ndash;0.937, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and academic self-efficacy (OR\u0026thinsp;=\u0026thinsp;1.018, 95% CI: 1.012\u0026ndash;1.024, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant predictors of latent profile membership. Specifically, more positive academic emotions and higher academic self-efficacy were associated with a greater likelihood of belonging to the Excellent Learning Strategy Class (vs. the Poor or Good classes).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eUndergraduate nursing students exhibit significant heterogeneity in learning strategies, which can be classified into three distinct latent profiles. Academic emotion and academic self-efficacy are key factors influencing these profiles. Nursing educators should prioritize targeted interventions for students in the Poor Learning Strategy Class (to build foundational learning skills) and Good Learning Strategy Class (to enhance deep learning and self-regulation). Such interventions could include academic emotion management workshops (e.g., stress reduction techniques, fostering positive learning attitudes) and self-efficacy enhancement programs. By addressing these influencing factors, nursing education can optimize students\u0026rsquo; learning strategies, ultimately improving their academic performance and supporting the development of competent, well-rounded nursing professionals.\u003c/p\u003e","manuscriptTitle":"Heterogeneity Study on Learning Strategies of Undergraduate Nursing Students Based on Latent Profile Analysis and Exploration of Influencing Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 05:10:22","doi":"10.21203/rs.3.rs-7821396/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-22T04:56:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T06:56:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-21T06:54:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-10-10T01:04:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"249c162d-bbd3-4d26-ae2c-43c378d69a6d","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-20T07:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 05:10:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7821396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7821396","identity":"rs-7821396","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}