The Latent Profiles of Time Attitudes and Positive Emotional Adjustment in Secondary Vocational Students

The Latent Profiles of Time Attitudes and Positive Emotional Adjustment in Secondary Vocational Students | 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 The Latent Profiles of Time Attitudes and Positive Emotional Adjustment in Secondary Vocational Students Xiaobin Ding, Xiangling Tu, Bo Wu, Donglin Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718305/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in BMC Psychology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Time attitude, as an affective dimension of time insight, is significantly related to adolescents’ adaptive and nonadaptive functioning. This study explored the types of time attitudes among Chinese secondary vocational students and assessed the relationship between different time attitude subgroupsand positive emotional adjustment. Methods A total of 2,310 secondary vocational students (M = 16.34, SD = 0.951; 41.7% female) participated in the study. Latent profile analysis was conducted using the Adolescent Time Attitude Scale, with positive emotional adjustment indicators analyzed as distal outcomes. Results The analysis revealed three time attitude profiles: Positive, Ambivalent, and Negative. Secondary vocational students in the Positive profile had the highest levels of life satisfaction and positive affect, but the lowest level of self-esteem; those in the Ambivalent profile had the lowest levels of life satisfaction and positive emotions, but the highest level of self-esteem. Conclusion Despite the limitations of the findings, they support existing research, enrich the theoretical framework in the field of time attitudes, and provide an empirical basis for the psychological adaptation of secondary vocational students. Secondary Vocational Students Time Attitudes Positive Emotional Adjustment Latent Profile Analysis Figures Figure 1 Introduction Adolescence is a crucial stage in an individual's development during which adolescents experience significant physical and psychological changes [ 1 , 2 ]. Erikson, in his theory of psychosocial development, stated that adolescence is a critical period in which an individual develops his or her self-identity, which is signaled by an understanding of childhood experiences (past), current self-perceptions (present), and future adult role expectations (future)[ 3 ]. As a special part of the adolescent group, secondary vocational students refer to the group of students who attend secondary vocational schools (including secondary specialized schools, technical schools and vocational high schools) after graduating from junior high school [ 4 ]. In recent years, with the rapid development of vocational education, the mental health of secondary vocational students has gradually become the focus of social concern. Research indicates that, compared to general high school students, secondary vocational students are more likely to experience mental health problems [ 5 ], problematic behaviors and emotional disorders [ 6 , 7 ]. As secondary vocational students may have experienced multiple setbacks in the past, such as academic failure, they may exhibit distinctive psychological characteristics toward the past, present, and future. Time attitude refers to an individual’s emotional experiences and cognitive evaluations of the past, present, and future [ 8 ]. A substantial body of research has found significant correlations between time attitudes and adaptive functioning among adolescent samples [ 9 , 10 ]. For instance, positive time attitudes show a significant positive correlation with higher academic achievement, optimism, and life satisfaction[ 11 – 13 ], whereas negative time attitudes are significantly negatively associated with psychological issues such as anxiety and depression [ 14 , 15 ]. These studies suggest that time attitudes may be a key psychological indicator for promoting healthy development in adolescents. For secondary vocational students, their unique growth backgrounds and educational environments may shape distinct psychological characteristics in their time attitudes, such as future uncertainty or negative evaluations of the past. These attitudes may further influence their emotional adjustment and mental health. Drawing on nearly 30 years of groundbreaking research, psychologist Zimbardo argues that our perception of time shapes how we view the world and live our lives, and that time attitudes are among the most influential factors in human behavior [ 16 ], and based on this research, developed the Zimbardo Time Perspective Inventory (ZTPI). The ZTPI consists of five subscales: Past Positive, Past Negative, Present Fatalistic, Present Hedonistic, and Future. Since its first validation in 1999[ 17 ], the ZTPI has become the most frequently used measure of time perspective in the extant literature [ 18 ], and has been closely related to many aspects of human activity [ 19 , 20 ]. However, the ZTPI was developed based on a sample of college students, has demonstrated controversial reliability and validity among adolescent groups, and lacks subscales to measure future negative attitudes [ 21 ]. Based on this, Mello and Worrell developed the Adolescent Time Attitude Inventory (ATI-TA), which evaluates both positive and negative attitudes toward three time periods [ 22 ]. The scale’s six-factor model has high reliability, validity, and measurement equivalence across age, gender, time, and culture in different cultural contexts [ 23 – 25 ]. Since the effective validation of the ATI-TA, a growing body of research has examined time attitudes in adolescents, with most studies focusing on the validation of the reliability of time attitude measurement instruments and exploring the relationship between time attitudes and psychological outcome variables. For example, researchers have explored the relationship between time attitudes and self-esteem, perceived stress, academic achievement, self-efficacy, substance use, and risky behaviors using a variable-centered approach [ 26 – 29 ]. In the research on time structure, individuals do not rely solely on a single time period to maintain a certain attitude towards it. Instead, they simultaneously hold interconnected attitudes toward past, present, and future time periods. Therefore, researchers have also begun to employ person-centered approach (such as cluster analysis and latent profile analysis) to study time structure. Person-centered approach identify homogeneous subgroups based on the response patterns of the sample to specific variables of interest, thereby providing greater specificity [ 30 ]. In other words, individuals with similar responses on the specified variables are grouped into more precise profiles or subgroups. The multidimensional structure of time attitudes is particularly well-suited for person-centered approach. Profiles can be derived from six variable-centered time attitude dimensions (positive past, negative past, positive present, negative present, positive future, and negative future), with each profile taking into account an individual’s positive and negative feelings towards all three time period simultaneously. Research has shown that time attitude profile types are better predictors of various behaviors and psychological outcomes for individuals than time attitude factor scores [ 31 ]. Buhl and Lindner first applied latent profile analysis to time attitude scale scores, identifying and naming six time attitude types by comparing the dimension scores with the sample mean [ 32 ]. Since then, numerous researchers have extensively explored the latent types of time attitudes in various cultural contexts using a person-centered approach. Of the relevant studies, five types of time attitude profiles have been found most commonly [ 9 , 11 , 24 , 33 , 34 ]. However, there have also been studies identifying four types within specific cultural groups [ 35 , 36 ], as well as studies identifying three temporal attitude profile types [ 37 ]. This indicates that there are differences in the number of profiles across samples and that the profile types are not entirely consistent. Specifically, Positives and Negatives were the more prevalent profile types, identified multiple times across studies [ 24 , 33 – 36 ]. At the same time, some less common profile types have caught the attention of researchers, such as Resilients [ 9 ] and Pessimists [ 11 ]. These heterogeneous features provide new perspectives for understanding the diversity of time attitudes. Emotional adjustment refers to an individual’s psychological and behavioral responses to environmental demands, reflecting the capacity for positive emotional regulation and self-control. This construct serves as one of the key indicators of adaptive functioning in environmental context [ 38 ]. Good emotional adjustment plays an important role in the positive development of adolescents. However, previous studies have mostly focused on the negative aspects of emotional adjustment, such as loneliness, depression, and anxiety [ 39 – 41 ]. Although negative emotional adjustment is important for the survival of individuals from the perspective of evolutionary psychology, in modern society, the needs of individuals have shifted from mere survival issues to growth and development. Positive emotional adjustment has a significant contribution to the long-term development of individuals [ 42 ]. Positive emotional adjustment is one of the core concepts in the research of positive psychology. Fredrickson proposed the Broaden-and-Build Theory, which suggests that positive emotions can broaden an individual’s cognitive scope and enhance their psychological resources, thus facilitating adjustment and development. Positive emotional adjustment typically includes positive affect, life satisfaction, and self-esteem [ 43 ]. Research variable-centered has shown that positive time attitudes are positively correlated with self-esteem, while negative time attitudes are negatively correlated with self-esteem [ 12 , 13 ]. Person-centered studies have found that individuals in the positive profile have the highest levels of self-esteem, while those in the negative profile have the lowest levels of self-esteem [ 26 ]. Other studies have also demonstrated that individuals with positive attitudes towards the past, present, and future exhibit higher levels of life satisfaction and positive affect, whereas those with negative time attitudes exhibit lower levels of life satisfaction and positive affect [ 44 – 46 ]. Previous research has demonstrated various outcomes associated with adolescent time attitude profiles; however, the specific time attitude profiles of secondary vocational students—a distinct adolescent subgroup—remain unexamined. In addition, the relationship between time attitude profiles and positive emotional adjustment (i.e., life satisfaction, positive affect, and self-esteem) among secondary vocational students has not been previously explored. In the present study, we first determined the time attitude profiles of secondary vocational students, and we hypothesized that we would find at least two profiles — a Positive profile that scored more positively for the three time periods, and a Negative profile that scored more negatively for the three time periods. Second, we explored the relationship between the identified time attitude profiles and positive emotional adjustment, with the time attitude profile serving as the independent variable. Based on previous research, we hypothesized that there are differences in positive emotional adjustment among different profiles, with individuals in the Positive profile having the highest levels of self-esteem, life satisfaction, and positive affect. Method Participants In this study, we utilized a convenience sampling approach by selecting a vocational high school in northwestern China for school-wide test. The survey was administered in a group (classroom-based) format. A total of 2,310 valid questionnaires were obtained, including 1,346(58.3%) males students and 964(41.7%) females students. The sample included 771(33.4%) students from the first year of vocational high school, 882(38.2%) from the second year, and 657(28.4%) from the third year. The mean age of participants was 16.34 years (SD = 0.951). Measures Adolescent Time Attitude Scale(ATAS) Time attitude was assessed using the Chinese version of the Adolescent Time Attitude Scale [ 47 ]. The scale includes six subscales: Past Positive, Past Negative, Present Positive, Present Negative, Future Positive, and Future Negative, with five items in each scale (1 = completely disagree, 5 = completely agree). In this study, Cronbach’s alpha coefficients for the six subscales were 0.808, 0.787, 0.825, 0.771, 0.792, and 0.681,, respectively. The Satisfaction with Life Scale(SWLS) Life satisfaction was measured using the Satisfaction with Life Scale [ 48 ], which consists of 5 items rated on a 7-point Likert scale (1 = not at all, 7 = fully). Higher scores indicate higher levels of life satisfaction. The Cronbach’s alpha in the present study was 0.773. Positive and Negative Affect Scale for Children (PANAS-C) Positive affect was assessed using the Chinese version of the Positive and Negative Affect Scale for Children [ 49 , 50 ]. The scale contains 30 items, inclueding two 15-item subscales measuring positive affect and negative affect. Only the positive affect subscale was used in this study, with items rated on a 5-point scale (1 = very slight or none at all, 5 = extremely strong). The Cronbach’s alpha coefficient of the positive affective subscale was: 0.917. Rosenberg Self-Esteem Scale (SES) Self-esteem was measured by the Chinese version of the Rosenberg Self-Esteem Scale [ 51 , 52 ], which consists of 10 items and on a 4-point scale (1 = very non-conforming, 4 = very conforming). Higher scores indicating higher levels of self-esteem. The Cronbach’s alpha in the present study was 0.821. Procedure The study was approved by the Ethics Committee of the School of Psychology at Northwest Normal University, China. Informed consent was obtained from participants and legal guardians before data collection. A psychology graduate student with professional training served as the primary examiner, administering paper questionnaires following standardized protocols. The examiner explained the survey content and research purpose to participants, while assuring participants of the data for academic research use only, etc. After students completed the questionnaire, it was collected on site. It took approximately 20 minutes to complete the questionnaire. Statistical Analyses Data were analyzed using Mplus 8.3 and SPSS 22.0. Descriptive statistics and Pearson correlations among study variables were computing using SPSS. Latent profile analysis (LPA) was conducted in Mplus to indentify latent subgroups of time attitudes. The optimal number of lantent classes was determined based on multiple fit indices. Specifically, the following indicators were used to determine the best classification model: The relative fit indices, Akaike Information Criterion (AIC), Bayesian Information Criteria (BIC), and adjusted BIC (aBIC), reflect the model fit. Lower AIC, BIC, and aBIC values indicate better model fit. Entropy reflects the reliability of classification, with values closer to 1 indicating more reliable classification. An Entropy value of at least 0.80 is recommended [ 53 ]. The Lo-Mendel–Rubin Likelihood Ratio Test (LMR-LRT) and the Bootstrapped Likelihood Ratio Test (BLRT) are used to compare the differences between classification methods with adjacent numbers of classes. Significant LMR and BLRT test results indicate that a k -class model fits significant better than the (k − 1)- class model [ 54 ]. Finally, based on the results of the optimal fit model and the number of final latent profiles, the relationship between the latent classification variable and positive emotional adjustment was examined using the robust three-step method (R3STEP) and the Bolck, Croon, and Hagenaars’s method (BCH) [ 55 ]. Results Preliminary Analyses Table 1 presents the means, standard deviations, and correlation among the study variables. Overall, positive time attitudes were significantly positively correlated with life satisfaction and positive affect, and significantly negatively correlated with self-esteem. In contrast, negative time attitudes were significantly negatively correlated with life satisfaction and positive affect, and significantly positively correlated with self-esteem. Table 1 Correlation between temporal attitude scale scores and other variables. PaP PaN PrP PrN FuP FuN LiS PoE SeE PaP 1 PaN − .537 ** 1 PrP .602 ** − .368 ** 1 PrN − .410 ** .595 ** − .584 ** 1 FuP .520 ** − .288 ** .573 ** − .363 ** 1 FuN − .304 ** .530 ** − .305 ** .557 ** − .509 ** 1 LiS .494 ** − .316 ** .620 ** − .421 ** .407 ** − .222 ** 1 PoE .336 ** − .174 ** .371 ** − .218 ** .352 ** − .185 ** .325 ** 1 SeE − .428 ** .452 ** − .469 ** .490 ** − .479 ** .479 ** − .403 ** − .363 ** 1 M 3.37 2.73 3.25 2.70 3.65 2.54 3.93 2.79 2.29 SD 0.74 0.76 0.70 0.70 0.66 0.64 1.15 0.70 0.45 Note : PaP = Past Positive, PaN = Past Negative, PrP = Present Positive, PrN = Present Negative, FuP = Future Positive, FuN = Future Negative, LiS = Life satisfaction, SeE = Self-esteem, PoE = Positive emotion. ** p<0.01 , * p<0.05 Potential Profile Analysis To determine the optimal number of time attitude profiles for secondary vocational students, modles with one to five classes were estimated based on the mean scores of the six time attitude dimensions. The model fit indices for each solution are presented in Table 2 . As shown in Table 2 , the values of AIC, BIC, and aBIC indices decreased with increasing model complexity, the Entropy value was the largest among the four classes of models, but the proportion of one category was only 2.4%, which was lower than the 5% of the sample, and the BLRT indices turned out to be consistently significant in all the models. Considering all fit indices, interpretability, and parsimony, the three-class model was selected as the optimal potential solution for the latent profile analysis of time attitudes. In addition, the accuracy of the classification results of the potential profile analysis was verified using discriminant analysis, and the average probability of the three potential profiles being attributed to the corresponding type ranged from 92–94%, indicating that the three-class model has a high classification accuracy. Table 2 Fitting index of the potential class model of time attitude. Type AIC BIC aBIC Entropy LMR BLRT Type probability 1 29422.550 29491.490 29453.364 1 2 26352.215 26461.370 26401.004 0.790 0.0000 0.0000 0.367/0.633 3 25385.137 25534.507 25451.900 0.856 0.0000 0.0000 0.067/0.657/0.276 4 24838.904 25028.489 24923.642 0.881 0.0009 0.0000 0.635/0.069/0.024/0.272 5 24241.146 24470.947 24343.859 0.848 0.0000 0.0000 0.336/0.508/0.057/0.027/0.072 6 24054.968 24324.983 24175.655 0.852 0.3750 0.0000 0.078/0.488/0.341/0.045/0.019/0.029 After identifying the three-class model as the optimal solution, further analysis was conducted to interpret the characteristics of each latent profile. Table 3 presents the mean scores of the ATA subscales across the three profiles. The profiles were labeled based on the patterns of scores and are illustrated in Fig. 1 . The first profile, labeled Negative (n = 154, 6.7%), exhibited scores above the sample mean on all three negative dimensions and scores below the sample mean on the three positive dimensions. The second profile, labeled Ambivalent (n = 1519, 65.7%), showed scores close to the sample mean on both positive and negative dimensions, reflecting a mixed or undifferentiated pattern of time attitudes. The third profile, labeled Positive (n = 637, 27.6%), was characterized by scores above the sample mean on all three positive time attitude dimensions and scores below the sample mean all three negative dimensions. Table 3 ༎ Latent profile means and frequencies Profile n(%) Means PaP PaN PrP PrN FuP FuN Negative 154(6.7) 2.122 3.763 2.133 3.673 2.830 3.122 Ambivalent 1519(65.7) 3.228 2.899 3.086 2.884 3.509 2.701 Positive 637(27.6) 4.039 2.061 3.932 2.024 4.195 2.016 Note : PaP = Past Positive, PaN = Past Negative, PrP = Present Positive, PrN = Present Negative, FuP = Future Positive, FuN = Future Negative. Positive emotional adjustment across time attitude profiles Based on the finalized potential profiles, the results of BCH’s chi-square test were utilized to verify whether there was a significant difference between individuals with different time attitude profiles on indicators related to positive emotional adjustment. The results in Table 4 show that based on the overall as well as the chi-square test results of two-by-two comparisons, there is a significant difference in positive emotional adjustment among individuals of the three time attitude profiles (p < 0.001). Overall, the Positive profile demonstrated the highest mean scores on life satisfaction and positive affect, followed by the Negative profile, with the Ambivalent profile showing the lowest scores. Conversely, for Self-Esteem, the Ambivalent profile had the highest mean scores, followed by the Negative profile, and the Positive profile had the lowest scores. Table 4 Difference test of positive emotional adjustment across time attitude profiles Variable Mean(SE) BCH χ2 C1 C2 C3 Overall C1 vs C2 C1 vs C3 C2 vs C3 LiS 3.739(0.027) 2.201(0.080) 4.837(0.042) 972.501 *** 315.514 *** 438.626 *** 848.384 *** PoE 2.680(0.018) 2.246(0.050) 3.179(0.032) 298.975 *** 63.846 *** 174.139 *** 250.645 *** SeE 2.373(0.010) 2.958(0.042) 1.934(0.017) 741.289 *** 173.559 *** 444.935 *** 505.947 *** Note : LiS = Life satisfaction, SeE = Self-esteem, PoE = Positive emotion, C1 = Negative, C2 = Ambivalent, C3 = Positive. Discussion In recent years, research on adolescents’ time attitudes has gradually increased. Building on previous research findings, the study focuses on students from vocational high schools, exploring the types of time attitudes among vocational high school students and their relationships with positive emotional adjustment. The aim is to better understand the unique characteristics of time attitudes within this particular group. Latent profile analysis identified three interpretable time attitude profiles: Positive, Ambivalent and Negative. Further analysis of the relationship between these three profile types and positive emotional adjustment revealed that — consistent with the research hypothesis — the Positive exhibited the highest levels of life satisfaction and positive affect. However, contrary to the hypothesis, the Positive displayed the lowest level of self-esteem, while the Ambivalent demonstrated the highest level of self-esteem. These findings will be discussed in the following section.. Latent profile analysis (LPA) is an person-centered research method by which to understand how different individuals combine positive and negative feelings about the past, present, and future as a means of identifying types of adolescents with similar time attitudes. The study finalized three time attitude profiles through latent profile analysis. This result is consistent with previous studies not only in the number of types, but also in the similarity of type characteristics [ 37 ]. Again using a large sample, Prow and peers found three time attitude profiles as Conflicted (8%), Ambivalent (75%), and Positive (17%), and the characteristics of the Positive and Ambivalent were exactly the same as in the present study, with slightly different percentages, and the Ambivalent had the highest percentage in all of them. The Conflict in Prow et al.’s study and the Negative in the present study were slightly different in terms of positive scores, but both had the smallest percentage of one group, while negative profiles consistent with the present study have been observed in other studies [ 33 , 34 ]. Positive time attitudes are strongly associated with life satisfaction and positive affect, which is similar to the findings of established studies [ 13 , 32 ]. Interestingly, in previous studies, the Negative group demonstrated the lowest level of life satisfaction [ 32 ], whereas in the present study, the Ambivalent group had the lowest levels of both satisfaction and positive affect. Individuals in the Ambivalent group may experience significant psychological conflict between positive and negative aspects. The conflict could contribute to emotional exhaustion, consequently diminishing their life satisfaction and positive affect. For example, they may exhibit positive emotional tendencies in some areas, while being strongly influenced by negative emotions in others. This contradictory psychological state makes it difficult for them to maintain high levels of life satisfaction. Regarding self-esteem, the variable-centered analyses in this study revealed negative correlations between positive time attitudes and self-esteem, and positive correlations between negative time attitudes and self-esteem. In the person-centered analysis, individuals in the Positive group had the lowest level of self-esteem and those in the Ambivalent group had the highest level of self-esteem, which is inconsistent with the hypotheses and existing research [ 12 , 26 ]. Worrel and Mello found that positive time attitudes were positively correlated with self-esteem, while negative time attitudes were negatively correlated with self-esteem. Andretta and peers found that the positive group had the highest self-esteem and the negative group had the lowest self-esteem. There are several possible reasons for this inconsistency. First, due to the relative disadvantage of secondary vocational students in terms of academic performance and social recognition, which leads to their lack of confidence in self-evaluation, some studies have shown that secondary vocational students generally have lower levels of self-esteem [ 56 ]. Second, time attitude is a complex multidimensional concept, including different cognitive and emotional tendencies towards the past, present and future. Secondary vocational student’s time attitudes may show specificity in different dimensions, for example, they may have more negative memories of the past and feel confused about the present and the future, and this complex structure of time attitudes may lead to an abnormal relationship with self-esteem. Third, as a specific group, the psychological characteristics and behavioral patterns of secondary vocational students may be quite different from those of other groups [ 5 – 7 ], and thus inconsistencies may occur when the findings of other groups are directly applied to secondary vocational students . This study also has several limitations. First, the sample was drawn from only one vocational high school, which may limit the generalizability of the findings to other types of vocational schools, such as specialized health vocational schools or police vocational schools. Future research could employ stratified sampling methods to select samples from different types of vocational high schools to enhance the applicability of the results. Second, although the sample size is relatively large, the sampling is limited to a county-level region in western China, which may not fully reflect the true situation of vocational high school students nationwide. Expanding the sampling to include regions with diverse geographical locations and varying levels of economic development would be conducive to the representativeness of the research findings. Third, the study employed a cross-sectional research design, which, while providing theoretical support for the positive impact of positive time attitudes on adolescents’ psychological adjustment, cannot reveal the causal relationships between time attitudes and psychological variables among vocational high school students, or their developmental trajectories over time. Future research should adopt longitudinal designs to track the development of time attitudes in secondary vocational students across different academic stages, exploring dynamic changes and their dynamic relationships with relevant psychological outcomes. In summary, despite these limitations, this study made significant progress in understanding the heterogeneity of time attitudes and their relationship with positive emotional adjustment among Chinese secondary vocational students. The results of the study identified three heterogeneous groups of time attitudes, with the Positive group having the highest life satisfaction and positive affect among secondary vocational students and the Ambivalent group having the highest level of self-esteem. These findings emphasize the importance of group differences in understanding the psychological development of secondary vocational students and add to the understanding of the diversity of time attitudes. Declarations Ethics approval and consent to participate All human experiments and use of human tissue samples conducted in this research strictly adhered to relevant ethical guidelines and regulations. This study has been approved by the Ethics Committee of the School of Psychology at Northwest Normal University (approval number: 2023093). Research approval encompassed obtaining consent to participate and publish from both adolescents and their parents. These approvals and registration ensure the ethical compliance of the research, as well as the protection and respect of the participants’ rights. We hereby confrm that all experimental details comply with the requirements and recommendations of the institution.We obtained informed consent from all individual participants included in the study. As the study involves participants with age less than 18, We ensure to provide a statement confrming that informed consent was obtained from their respective guardians. Clinical trial number: Not applicable. Consent for publication Not applicable. Availability of data and materials Data will be available upon reasonable request. Competing interests The authors declare no competing interests. Funding This research was supported by the Regional Project of the National Natural Science Foundation of China (31960181, 32360213). Authors' contributions DXB designed the research plan and reviewed the manuscript. 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The Adolescent Time Attitude Scale–English. University of California, Berkeley, CA: Author. 2007. McKay M, Healy C, O’Donnell L. The Adolescent and Adult Time Inventory-Time Attitudes Scale: A Comprehensive Review and Meta-Analysis of Psychometric Studies. Journal of Personality Assessment. 2021; 103(5): 576-587. https://doi.org/10.1080/00223891.2020.1818573. Worrell FC, Mello ZR, Laghi F, Baiocco R, & Lonigro A. Time Perspective Constructs in Albanian and Italian Adolescents: Exploratory Analyses. Psychological Reports. 2021; 124(2): 693-719. https://doi.org/10.1177/0033294120913493. Yacob ET, Bezabih BM, Worrell FC, Mello ZR. Measuring time perspective in Ethiopian young adults using the Adolescent and Adult Time Inventory (AATI). Journal of Psychology in Africa. 2020; 30(6): 520-528. https://doi.org/10.1080/14330237.2020.1842598. Andretta JR, Worrell FC, Mello ZR. Predicting educational outcomes and psychological wellbeing in adolescents using time attitude profiles. Psychology in the Schools. 2014; 51(5): 434-451. https://doi.org/10.1002/pits.21762. Finan LJ, Linden-Carmichael AN, Adams AR, Youngquist A, Lipperman-Kreda S, Mello ZR. Time perspective and substance use: an examination across three adolescent samples. Addiction Research & Theory. 2022; 30(2): 112-118.https://doi.org/10.1080/16066359.2021.1948537. Mello ZR, Zhang JW, Barber SJ, Paoloni VC, Howell RT, Worrell FC. Psychometric properties of time attitude scores in young, middle, and older adult samples. Personality & Individual Differences. 2016; 101: 57-61. https://doi.org/10.1016/j.paid.2016.05.037. Wang J, Sun Y. Time flies, but you’re in control: the mediating effect of self-control between time attitude and academic procrastination. BMC Psychology. 2023; 11(1): 393. https://doi.org/10.1186/s40359-023-01438-2. Nylund-Gibson KL, Ryan Grimm R, Quirk M, Furlong M. A latent transition mixture model using the three-step speciffcation. Structural Equation Modeling: A Multidisciplinary Journal. 2014; 21(3): 439-454. https://doi.org/10.1080/10705511.2014.915375. Worrell FC, Andretta JR. Time attitude profiles in American adolescents: Educational and psychological correlates. Research in Human Development. 2019; 16(2): 102-118. https://doi.org/10.1080/15427609.2019.1635860. Buhl M, Lindner D. Time perspectives during adolescence: Measure, profiles and connections with personality characteristics and school experiences. Diskurs Kindheits Und Jugendforschung. 2009; 4(2): 197-216. McKay MT, Donnelly P, Paradis KF, Horgan P, Brennan CJ, Cole JC, Worrell FC. Time to look at self-rated health: Do time attitudes scores explain variance in self-rated health beyond health indicators? Personality & Individual Differences. 2024; 217: 112454. https://doi.org/10.1016/j.paid.2023.112454. Tejada-Gallardo C, Blasco-Belled A, Alsinet C. Impact of a School-Based Multicomponent Positive Psychology Intervention on Adolescents’ Time Attitudes: A Latent Transition Analysis. Journal of Youth and Adolescence . 2022; 51(5): 1002-1016. https://doi.org/10.1007/s10964-021-01562-5. Tejada-Gallardo C, Blasco-Belled A, Alsinet C. Feeling positive towards time: How time attitude profiles are related to mental health in adolescents. Journal of Adolescence . 2021; 89: 84-94. https://doi.org/10.1016/j.adolescence.2021.04.002. Wells KE, McKay MT, Morgan GB, Worrell FC. Time attitudes predict changes in adolescent self-efficacy: A 24-month latent transition mover-stayer analysis. Journal of Adolescence . 2018; 62: 27-37. https://doi.org/10.1016/j.adolescence.2017.10.005. Prow RM, Worrell FC, Andretta JR, Mello ZR. Demographic Differences in Adolescent Time Attitude Profiles in an Urban High School: A Person-Oriented Analysis Using Model-Based Clustering. Berkeley Review of Education. 2016; 6(1): 79-95.https://doi.org/10.5070/B86110030. Sekar J, Lawrence A. Emotional adaptation and its impact on adolescent development. Journal of Adolescence. 2016; 50: 1-12. Ding X, Chen X, Fu R, Li D, Liu J. Relations of Shyness and Unsociability with Adjustment in Migrant and Non-migrant Children in Urban China. Journal of Abnormal Child Psychology . 2020; 48(2): 289-300. https://doi.org/10.1007/s10802-019-00583-w. Peng S, Niu G, Wang X, Zhang H, Hu J. Effects of parental autonomy support on adolescents' positive emotional adjustment: a model of the mediating and moderating roles of basic psychological needs satisfaction. Psychological Development and Education. 2021; 37(2): 240-248. https://doi.org/ 10.16187/j.cnki.issn1001-4918.2021.02.11. Zhang S, Dong Y, Chen F, Alshen H, Ding X, Zhu Z. The relationship between children's social self-perception and emotional adjustment problems: the role of avoidance coping strategies and social sensitivity. Studies of Psychology and Behavior. 2023; 21(3): 336-343. https://doi.org/10.12139/j.1672-0628.2023.03.007. Fredrickson BL. The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist. 2001; 56(3): 218-226. https://doi.org/10.1037/0003-066X.56.3.218. Cheung CS, Pomerantz EM. Parents’ involvement in children’s learning in the United States and China: Implications for children’s academic and emotional adjustment. Child Development. 2011; 82(3): 932-950. https://doi.org/10.1111/j.1467-8624.2011.01582.x. Diaconu-Gherasim LR, Mardari CR, Măirean C. The relation between time perspectives and well-being: A meta-analysis on research. Current Psychology . 2023; 42(7): 5951-5963. https://doi.org/10.1007/s12144-021-01949-4. Kooij DT, Kanfer R, Betts M, Rudolph CW. Future time perspective: A systematic review and meta-analysis. Journal of Applied Psychology . 2018; 103(8): 867-893. https://doi.org/10.1037/apl0000306. Măirean C, Diaconu-Gherasim LR. Adolescents’ subjective well-being: The role of adolescents’ and mothers’ time perspectivess. Time & Society . 2019; 28(3): 1084-1104. https://doi.org/10.1177/0961463X17752282. Li X, Mao Y, L H, Wang Y. Reliability test of the Chinese version of the Time Attitude Scale. Chinese Journal of Clinical Psychology. 2021; 29(2): 375-379. https://doi.org/10.16128/j.cnki.1005-3611.2021.02.032. Diener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. Journal of Personality Assessment. 1985; 49(1): 71-75. https://doi.org/10.1207/s15327752jpa4901_13. Laurent J, Catanzaro SJ, Joiner TE. A measure of positive and negative affect for children: Scale development and preliminary validation. Psychological Assessment. 1999; 11(3): 326-338. https://doi.org/10.1037/1040-3590.11.3.326. Pang T, Ding X, Sang B, Liu Y, Xie S, Feng X. A preliminary investigation of the reliability and validity of the Positive and Negative Affect Scale for Children (PANAS-C). Chinese Journal of Clinical Psychology. 2015; 3: 397-400: https://doi.org/10.16128/j.cnki.1005-3611.2015.03.004. Rosenberg M. Society and the adolescent self-image . Princeton, NJ: Princeton University Press. 1965. Wang X, Wang X, Ma H. Mental health assessment scale (revised edition). 1999; (pp. 318-320). Beijing: Chinese Mental Health Magazine Press. Jung T, Wickrama KAS. An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass. 2008; 2(1): 302-317. https://doi.org/10.1111/j.1751-9004.2007.00054.x. Nylund KL, Asparouhov T, Muthén, BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural equation modeling: A multidisciplinary Journal. 2007; 14(4): 535-569. https://doi.org/10.1080/10705510701575396. Asparouhov T, Muthén B. Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes. 2014; 21(2): 1-22. Ni H. Analysis of the current situation of self-esteem level of secondary vocational students --Taking Jiangsu Agricultural and Animal Husbandry Science and Technology Vocational College as an example. Journal of Jiamusi College of Education. 2017; 12: 4-5. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6718305","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465969314,"identity":"9d86a527-aef9-4441-8ec2-8136fd33eb4a","order_by":0,"name":"Xiaobin Ding","email":"","orcid":"","institution":"Northwest Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Ding","suffix":""},{"id":465969315,"identity":"8cf84bc5-febc-4a9e-8498-4a4b6b834c6c","order_by":1,"name":"Xiangling Tu","email":"","orcid":"","institution":"Northwest Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiangling","middleName":"","lastName":"Tu","suffix":""},{"id":465969316,"identity":"33c8955d-461f-4247-a294-cc9dfa4d8c51","order_by":2,"name":"Bo Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACAxCRUCHBww8VYGwgSsuDMzZykg2kaGF82JZmbHCAWC3m7D1mEolthxM33+4xe8zDYCO74QDzswf4tFj2nDGTSDh3OHHbnTPmxjwMacYbDrCZG+B12I3cbRIJZUAtQIY0D8PhxA0HeNgkCGthAzpsBljLf2K1gLwvAdZygLAWy57zny0SgIEscSP/m+Qcg2TjmYfZzPBqMWdvS7z5AxSVM9LSJN5U2Mn2HW9+hlcLujuBmJkE9aNgFIyCUTAKsAMAXFhLBsN6ZG4AAAAASUVORK5CYII=","orcid":"","institution":"Northwest Normal University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Wu","suffix":""},{"id":465969317,"identity":"a737276f-565e-4151-8ea5-acdebdcef33f","order_by":3,"name":"Donglin Jin","email":"","orcid":"","institution":"Lanzhou Zhicheng Academy","correspondingAuthor":false,"prefix":"","firstName":"Donglin","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2025-05-21 16:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6718305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6718305/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-025-03928-x","type":"published","date":"2026-01-20T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83967912,"identity":"600abfdf-f489-4bab-b979-6dd7b9c22810","added_by":"auto","created_at":"2025-06-05 07:16:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32767,"visible":true,"origin":"","legend":"\u003cp\u003eVisual representation of time attitudes profiles\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6718305/v1/2f4d092c8cbb0bb7513e959d.png"},{"id":101151901,"identity":"411ae2fe-7710-4cdd-bbc3-e7e55911b31a","added_by":"auto","created_at":"2026-01-26 16:07:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":918177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6718305/v1/fa05f9ef-4921-49d6-ad24-bcaac46513ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Latent Profiles of Time Attitudes and Positive Emotional Adjustment in Secondary Vocational Students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescence is a crucial stage in an individual's development during which adolescents experience significant physical and psychological changes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Erikson, in his theory of psychosocial development, stated that adolescence is a critical period in which an individual develops his or her self-identity, which is signaled by an understanding of childhood experiences (past), current self-perceptions (present), and future adult role expectations (future)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a special part of the adolescent group, secondary vocational students refer to the group of students who attend secondary vocational schools (including secondary specialized schools, technical schools and vocational high schools) after graduating from junior high school [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, with the rapid development of vocational education, the mental health of secondary vocational students has gradually become the focus of social concern. Research indicates that, compared to general high school students, secondary vocational students are more likely to experience mental health problems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], problematic behaviors and emotional disorders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As secondary vocational students may have experienced multiple setbacks in the past, such as academic failure, they may exhibit distinctive psychological characteristics toward the past, present, and future.\u003c/p\u003e \u003cp\u003eTime attitude refers to an individual\u0026rsquo;s emotional experiences and cognitive evaluations of the past, present, and future [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A substantial body of research has found significant correlations between time attitudes and adaptive functioning among adolescent samples [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, positive time attitudes show a significant positive correlation with higher academic achievement, optimism, and life satisfaction[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], whereas negative time attitudes are significantly negatively associated with psychological issues such as anxiety and depression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These studies suggest that time attitudes may be a key psychological indicator for promoting healthy development in adolescents. For secondary vocational students, their unique growth backgrounds and educational environments may shape distinct psychological characteristics in their time attitudes, such as future uncertainty or negative evaluations of the past. These attitudes may further influence their emotional adjustment and mental health.\u003c/p\u003e \u003cp\u003eDrawing on nearly 30 years of groundbreaking research, psychologist Zimbardo argues that our perception of time shapes how we view the world and live our lives, and that time attitudes are among the most influential factors in human behavior [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and based on this research, developed the Zimbardo Time Perspective Inventory (ZTPI). The ZTPI consists of five subscales: Past Positive, Past Negative, Present Fatalistic, Present Hedonistic, and Future. Since its first validation in 1999[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the ZTPI has become the most frequently used measure of time perspective in the extant literature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and has been closely related to many aspects of human activity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the ZTPI was developed based on a sample of college students, has demonstrated controversial reliability and validity among adolescent groups, and lacks subscales to measure future negative attitudes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on this, Mello and Worrell developed the Adolescent Time Attitude Inventory (ATI-TA), which evaluates both positive and negative attitudes toward three time periods [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The scale\u0026rsquo;s six-factor model has high reliability, validity, and measurement equivalence across age, gender, time, and culture in different cultural contexts [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Since the effective validation of the ATI-TA, a growing body of research has examined time attitudes in adolescents, with most studies focusing on the validation of the reliability of time attitude measurement instruments and exploring the relationship between time attitudes and psychological outcome variables. For example, researchers have explored the relationship between time attitudes and self-esteem, perceived stress, academic achievement, self-efficacy, substance use, and risky behaviors using a variable-centered approach [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the research on time structure, individuals do not rely solely on a single time period to maintain a certain attitude towards it. Instead, they simultaneously hold interconnected attitudes toward past, present, and future time periods. Therefore, researchers have also begun to employ person-centered approach (such as cluster analysis and latent profile analysis) to study time structure. Person-centered approach identify homogeneous subgroups based on the response patterns of the sample to specific variables of interest, thereby providing greater specificity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In other words, individuals with similar responses on the specified variables are grouped into more precise profiles or subgroups. The multidimensional structure of time attitudes is particularly well-suited for person-centered approach. Profiles can be derived from six variable-centered time attitude dimensions (positive past, negative past, positive present, negative present, positive future, and negative future), with each profile taking into account an individual\u0026rsquo;s positive and negative feelings towards all three time period simultaneously. Research has shown that time attitude profile types are better predictors of various behaviors and psychological outcomes for individuals than time attitude factor scores [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBuhl and Lindner first applied latent profile analysis to time attitude scale scores, identifying and naming six time attitude types by comparing the dimension scores with the sample mean [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Since then, numerous researchers have extensively explored the latent types of time attitudes in various cultural contexts using a person-centered approach. Of the relevant studies, five types of time attitude profiles have been found most commonly [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, there have also been studies identifying four types within specific cultural groups [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], as well as studies identifying three temporal attitude profile types [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This indicates that there are differences in the number of profiles across samples and that the profile types are not entirely consistent. Specifically, Positives and Negatives were the more prevalent profile types, identified multiple times across studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. At the same time, some less common profile types have caught the attention of researchers, such as Resilients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Pessimists [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These heterogeneous features provide new perspectives for understanding the diversity of time attitudes.\u003c/p\u003e \u003cp\u003eEmotional adjustment refers to an individual\u0026rsquo;s psychological and behavioral responses to environmental demands, reflecting the capacity for positive emotional regulation and self-control. This construct serves as one of the key indicators of adaptive functioning in environmental context [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Good emotional adjustment plays an important role in the positive development of adolescents. However, previous studies have mostly focused on the negative aspects of emotional adjustment, such as loneliness, depression, and anxiety [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Although negative emotional adjustment is important for the survival of individuals from the perspective of evolutionary psychology, in modern society, the needs of individuals have shifted from mere survival issues to growth and development. Positive emotional adjustment has a significant contribution to the long-term development of individuals [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePositive emotional adjustment is one of the core concepts in the research of positive psychology. Fredrickson proposed the Broaden-and-Build Theory, which suggests that positive emotions can broaden an individual\u0026rsquo;s cognitive scope and enhance their psychological resources, thus facilitating adjustment and development. Positive emotional adjustment typically includes positive affect, life satisfaction, and self-esteem [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Research variable-centered has shown that positive time attitudes are positively correlated with self-esteem, while negative time attitudes are negatively correlated with self-esteem [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Person-centered studies have found that individuals in the positive profile have the highest levels of self-esteem, while those in the negative profile have the lowest levels of self-esteem [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Other studies have also demonstrated that individuals with positive attitudes towards the past, present, and future exhibit higher levels of life satisfaction and positive affect, whereas those with negative time attitudes exhibit lower levels of life satisfaction and positive affect [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePrevious research has demonstrated various outcomes associated with adolescent time attitude profiles; however, the specific time attitude profiles of secondary vocational students\u0026mdash;a distinct adolescent subgroup\u0026mdash;remain unexamined. In addition, the relationship between time attitude profiles and positive emotional adjustment (i.e., life satisfaction, positive affect, and self-esteem) among secondary vocational students has not been previously explored. In the present study, we first determined the time attitude profiles of secondary vocational students, and we hypothesized that we would find at least two profiles \u0026mdash; a Positive profile that scored more positively for the three time periods, and a Negative profile that scored more negatively for the three time periods. Second, we explored the relationship between the identified time attitude profiles and positive emotional adjustment, with the time attitude profile serving as the independent variable. Based on previous research, we hypothesized that there are differences in positive emotional adjustment among different profiles, with individuals in the Positive profile having the highest levels of self-esteem, life satisfaction, and positive affect.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eIn this study, we utilized a convenience sampling approach by selecting a vocational high school in northwestern China for school-wide test. The survey was administered in a group (classroom-based) format. A total of 2,310 valid questionnaires were obtained, including 1,346(58.3%) males students and 964(41.7%) females students. The sample included 771(33.4%) students from the first year of vocational high school, 882(38.2%) from the second year, and 657(28.4%) from the third year. The mean age of participants was 16.34 years (SD\u0026thinsp;=\u0026thinsp;0.951).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAdolescent Time Attitude Scale(ATAS)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTime attitude was assessed using the Chinese version of the Adolescent Time Attitude Scale [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The scale includes six subscales: Past Positive, Past Negative, Present Positive, Present Negative, Future Positive, and Future Negative, with five items in each scale (1\u0026thinsp;=\u0026thinsp;completely disagree, 5\u0026thinsp;=\u0026thinsp;completely agree). In this study, Cronbach\u0026rsquo;s alpha coefficients for the six subscales were 0.808, 0.787, 0.825, 0.771, 0.792, and 0.681,, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Satisfaction with Life Scale(SWLS)\u003c/h3\u003e\n\u003cp\u003eLife satisfaction was measured using the Satisfaction with Life Scale [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which consists of 5 items rated on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;not at all, 7\u0026thinsp;=\u0026thinsp;fully). Higher scores indicate higher levels of life satisfaction. The Cronbach\u0026rsquo;s alpha in the present study was 0.773.\u003c/p\u003e\n\u003ch3\u003ePositive and Negative Affect Scale for Children (PANAS-C)\u003c/h3\u003e\n\u003cp\u003ePositive affect was assessed using the Chinese version of the Positive and Negative Affect Scale for Children [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The scale contains 30 items, inclueding two 15-item subscales measuring positive affect and negative affect. Only the positive affect subscale was used in this study, with items rated on a 5-point scale (1\u0026thinsp;=\u0026thinsp;very slight or none at all, 5\u0026thinsp;=\u0026thinsp;extremely strong). The Cronbach\u0026rsquo;s alpha coefficient of the positive affective subscale was: 0.917.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRosenberg Self-Esteem Scale (SES)\u003c/h2\u003e \u003cp\u003eSelf-esteem was measured by the Chinese version of the Rosenberg Self-Esteem Scale [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which consists of 10 items and on a 4-point scale (1\u0026thinsp;=\u0026thinsp;very non-conforming, 4\u0026thinsp;=\u0026thinsp;very conforming). Higher scores indicating higher levels of self-esteem. The Cronbach\u0026rsquo;s alpha in the present study was 0.821.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Ethics Committee of the School of Psychology at Northwest Normal University, China. Informed consent was obtained from participants and legal guardians before data collection. A psychology graduate student with professional training served as the primary examiner, administering paper questionnaires following standardized protocols. The examiner explained the survey content and research purpose to participants, while assuring participants of the data for academic research use only, etc. After students completed the questionnaire, it was collected on site. It took approximately 20 minutes to complete the questionnaire.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eData were analyzed using Mplus 8.3 and SPSS 22.0. Descriptive statistics and Pearson correlations among study variables were computing using SPSS. Latent profile analysis (LPA) was conducted in Mplus to indentify latent subgroups of time attitudes. The optimal number of lantent classes was determined based on multiple fit indices.\u003c/p\u003e \u003cp\u003eSpecifically, the following indicators were used to determine the best classification model: The relative fit indices, Akaike Information Criterion (AIC), Bayesian Information Criteria (BIC), and adjusted BIC (aBIC), reflect the model fit. Lower AIC, BIC, and aBIC values indicate better model fit. Entropy reflects the reliability of classification, with values closer to 1 indicating more reliable classification. An Entropy value of at least 0.80 is recommended [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The Lo-Mendel\u0026ndash;Rubin Likelihood Ratio Test (LMR-LRT) and the Bootstrapped Likelihood Ratio Test (BLRT) are used to compare the differences between classification methods with adjacent numbers of classes. Significant LMR and BLRT test results indicate that a \u003cem\u003ek\u003c/em\u003e-class model fits significant better than the \u003cem\u003e(k\u0026thinsp;\u0026minus;\u0026thinsp;1)-\u003c/em\u003eclass model [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Finally, based on the results of the optimal fit model and the number of final latent profiles, the relationship between the latent classification variable and positive emotional adjustment was examined using the robust three-step method (R3STEP) and the Bolck, Croon, and Hagenaars\u0026rsquo;s method (BCH) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary Analyses\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the means, standard deviations, and correlation among the study variables. Overall, positive time attitudes were significantly positively correlated with life satisfaction and positive affect, and significantly negatively correlated with self-esteem. In contrast, negative time attitudes were significantly negatively correlated with life satisfaction and positive affect, and significantly positively correlated with self-esteem.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between temporal attitude scale scores and other variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv 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\u003cp\u003e\u0026minus;\u0026thinsp;.421\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.407\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.222\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.336\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.174\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.371\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.218\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.352\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.325\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.428\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.452\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.469\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.490\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.479\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.479\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.403\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.363\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003ePaP\u0026thinsp;=\u0026thinsp;Past Positive, PaN\u0026thinsp;=\u0026thinsp;Past Negative, PrP\u0026thinsp;=\u0026thinsp;Present Positive, PrN\u0026thinsp;=\u0026thinsp;Present Negative, FuP\u0026thinsp;=\u0026thinsp;Future Positive, FuN\u0026thinsp;=\u0026thinsp;Future Negative, LiS\u0026thinsp;=\u0026thinsp;Life satisfaction, SeE\u0026thinsp;=\u0026thinsp;Self-esteem, PoE\u0026thinsp;=\u0026thinsp;Positive emotion.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.01\u003c/em\u003e, \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePotential Profile Analysis\u003c/h2\u003e \u003cp\u003eTo determine the optimal number of time attitude profiles for secondary vocational students, modles with one to five classes were estimated based on the mean scores of the six time attitude dimensions. The model fit indices for each solution are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the values of AIC, BIC, and aBIC indices decreased with increasing model complexity, the Entropy value was the largest among the four classes of models, but the proportion of one category was only 2.4%, which was lower than the 5% of the sample, and the BLRT indices turned out to be consistently significant in all the models. Considering all fit indices, interpretability, and parsimony, the three-class model was selected as the optimal potential solution for the latent profile analysis of time attitudes. In addition, the accuracy of the classification results of the potential profile analysis was verified using discriminant analysis, and the average probability of the three potential profiles being attributed to the corresponding type ranged from 92\u0026ndash;94%, indicating that the three-class model has a high classification accuracy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFitting index of the potential class model of time attitude.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eaBIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eType probability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29422.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29491.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29453.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26352.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26461.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26401.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.367/0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25385.137\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e25534.507\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e25451.900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.856\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.067/0.657/0.276\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24838.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25028.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24923.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.635/0.069/0.024/0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24241.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24470.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24343.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.336/0.508/0.057/0.027/0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24054.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24324.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24175.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.078/0.488/0.341/0.045/0.019/0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter identifying the three-class model as the optimal solution, further analysis was conducted to interpret the characteristics of each latent profile. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the mean scores of the ATA subscales across the three profiles. The profiles were labeled based on the patterns of scores and are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe first profile, labeled \u003cb\u003eNegative\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;154, 6.7%), exhibited scores above the sample mean on all three negative dimensions and scores below the sample mean on the three positive dimensions. The second profile, labeled \u003cb\u003eAmbivalent\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;1519, 65.7%), showed scores close to the sample mean on both positive and negative dimensions, reflecting a mixed or undifferentiated pattern of time attitudes. The third profile, labeled \u003cb\u003ePositive\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;637, 27.6%), was characterized by scores above the sample mean on all three positive time attitude dimensions and scores below the sample mean all three negative dimensions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e༎ Latent profile means and frequencies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eMeans\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePaP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePaN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFuP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFuN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmbivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1519(65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e637(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003ePaP\u0026thinsp;=\u0026thinsp;Past Positive, PaN\u0026thinsp;=\u0026thinsp;Past Negative, PrP\u0026thinsp;=\u0026thinsp;Present Positive, PrN\u0026thinsp;=\u0026thinsp;Present Negative, FuP\u0026thinsp;=\u0026thinsp;Future Positive, FuN\u0026thinsp;=\u0026thinsp;Future Negative.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePositive emotional adjustment across time attitude profiles\u003c/h2\u003e \u003cp\u003eBased on the finalized potential profiles, the results of BCH\u0026rsquo;s chi-square test were utilized to verify whether there was a significant difference between individuals with different time attitude profiles on indicators related to positive emotional adjustment. The results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that based on the overall as well as the chi-square test results of two-by-two comparisons, there is a significant difference in positive emotional adjustment among individuals of the three time attitude profiles (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, the Positive profile demonstrated the highest mean scores on life satisfaction and positive affect, followed by the Negative profile, with the Ambivalent profile showing the lowest scores. Conversely, for Self-Esteem, the Ambivalent profile had the highest mean scores, followed by the Negative profile, and the Positive profile had the lowest scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference test of positive emotional adjustment across time attitude profiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eBCH χ2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC1 vs C2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC1 vs C3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC2 vs C3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLiS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.739(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.201(0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.837(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e972.501\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e315.514\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e438.626\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e848.384\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.680(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.246(0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.179(0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e298.975\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.846\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e174.139\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e250.645\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.373(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.958(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.934(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e741.289\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e173.559\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e444.935\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e505.947\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eLiS\u0026thinsp;=\u0026thinsp;Life satisfaction, SeE\u0026thinsp;=\u0026thinsp;Self-esteem, PoE\u0026thinsp;=\u0026thinsp;Positive emotion, C1\u0026thinsp;=\u0026thinsp;Negative, C2\u0026thinsp;=\u0026thinsp;Ambivalent, C3\u0026thinsp;=\u0026thinsp;Positive.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, research on adolescents\u0026rsquo; time attitudes has gradually increased. Building on previous research findings, the study focuses on students from vocational high schools, exploring the types of time attitudes among vocational high school students and their relationships with positive emotional adjustment. The aim is to better understand the unique characteristics of time attitudes within this particular group. Latent profile analysis identified three interpretable time attitude profiles: Positive, Ambivalent and Negative. Further analysis of the relationship between these three profile types and positive emotional adjustment revealed that \u0026mdash; consistent with the research hypothesis \u0026mdash; the Positive exhibited the highest levels of life satisfaction and positive affect. However, contrary to the hypothesis, the Positive displayed the lowest level of self-esteem, while the Ambivalent demonstrated the highest level of self-esteem. These findings will be discussed in the following section..\u003c/p\u003e \u003cp\u003eLatent profile analysis (LPA) is an person-centered research method by which to understand how different individuals combine positive and negative feelings about the past, present, and future as a means of identifying types of adolescents with similar time attitudes. The study finalized three time attitude profiles through latent profile analysis. This result is consistent with previous studies not only in the number of types, but also in the similarity of type characteristics [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Again using a large sample, Prow and peers found three time attitude profiles as Conflicted (8%), Ambivalent (75%), and Positive (17%), and the characteristics of the Positive and Ambivalent were exactly the same as in the present study, with slightly different percentages, and the Ambivalent had the highest percentage in all of them. The Conflict in Prow et al.\u0026rsquo;s study and the Negative in the present study were slightly different in terms of positive scores, but both had the smallest percentage of one group, while negative profiles consistent with the present study have been observed in other studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePositive time attitudes are strongly associated with life satisfaction and positive affect, which is similar to the findings of established studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Interestingly, in previous studies, the Negative group demonstrated the lowest level of life satisfaction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], whereas in the present study, the Ambivalent group had the lowest levels of both satisfaction and positive affect. Individuals in the Ambivalent group may experience significant psychological conflict between positive and negative aspects. The conflict could contribute to emotional exhaustion, consequently diminishing their life satisfaction and positive affect. For example, they may exhibit positive emotional tendencies in some areas, while being strongly influenced by negative emotions in others. This contradictory psychological state makes it difficult for them to maintain high levels of life satisfaction.\u003c/p\u003e \u003cp\u003eRegarding self-esteem, the variable-centered analyses in this study revealed negative correlations between positive time attitudes and self-esteem, and positive correlations between negative time attitudes and self-esteem. In the person-centered analysis, individuals in the Positive group had the lowest level of self-esteem and those in the Ambivalent group had the highest level of self-esteem, which is inconsistent with the hypotheses and existing research [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Worrel and Mello found that positive time attitudes were positively correlated with self-esteem, while negative time attitudes were negatively correlated with self-esteem. Andretta and peers found that the positive group had the highest self-esteem and the negative group had the lowest self-esteem. There are several possible reasons for this inconsistency.\u003c/p\u003e \u003cp\u003eFirst, due to the relative disadvantage of secondary vocational students in terms of academic performance and social recognition, which leads to their lack of confidence in self-evaluation, some studies have shown that secondary vocational students generally have lower levels of self-esteem [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Second, time attitude is a complex multidimensional concept, including different cognitive and emotional tendencies towards the past, present and future. Secondary vocational student\u0026rsquo;s time attitudes may show specificity in different dimensions, for example, they may have more negative memories of the past and feel confused about the present and the future, and this complex structure of time attitudes may lead to an abnormal relationship with self-esteem. Third, as a specific group, the psychological characteristics and behavioral patterns of secondary vocational students may be quite different from those of other groups [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and thus inconsistencies may occur when the findings of other groups are directly applied to secondary vocational students .\u003c/p\u003e \u003cp\u003eThis study also has several limitations. First, the sample was drawn from only one vocational high school, which may limit the generalizability of the findings to other types of vocational schools, such as specialized health vocational schools or police vocational schools. Future research could employ stratified sampling methods to select samples from different types of vocational high schools to enhance the applicability of the results. Second, although the sample size is relatively large, the sampling is limited to a county-level region in western China, which may not fully reflect the true situation of vocational high school students nationwide. Expanding the sampling to include regions with diverse geographical locations and varying levels of economic development would be conducive to the representativeness of the research findings.\u003c/p\u003e \u003cp\u003eThird, the study employed a cross-sectional research design, which, while providing theoretical support for the positive impact of positive time attitudes on adolescents\u0026rsquo; psychological adjustment, cannot reveal the causal relationships between time attitudes and psychological variables among vocational high school students, or their developmental trajectories over time. Future research should adopt longitudinal designs to track the development of time attitudes in secondary vocational students across different academic stages, exploring dynamic changes and their dynamic relationships with relevant psychological outcomes.\u003c/p\u003e \u003cp\u003eIn summary, despite these limitations, this study made significant progress in understanding the heterogeneity of time attitudes and their relationship with positive emotional adjustment among Chinese secondary vocational students. The results of the study identified three heterogeneous groups of time attitudes, with the Positive group having the highest life satisfaction and positive affect among secondary vocational students and the Ambivalent group having the highest level of self-esteem. These findings emphasize the importance of group differences in understanding the psychological development of secondary vocational students and add to the understanding of the diversity of time attitudes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll human experiments and use of human tissue samples conducted in this research strictly adhered to relevant ethical guidelines and regulations. This study has been approved by the Ethics Committee of the School of Psychology at Northwest Normal University (approval number: 2023093).\u0026nbsp;Research approval encompassed obtaining consent to participate and publish from both adolescents and their parents. These approvals and registration ensure the ethical compliance of the research, as well as the protection and respect of the participants’ rights. We hereby confrm that all experimental details comply with the requirements and recommendations of the institution.We obtained informed consent from all individual participants included in the study. As the study involves participants with age less than 18, We ensure to provide a statement confrming that informed consent was obtained from their respective guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Regional Project of the National Natural Science Foundation of China (31960181, 32360213).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDXB designed the research plan and reviewed the manuscript. TXL was responsible for data analysis and drafting the original manuscript. WB reviewed the manuscript and provided key suggestions. JDL was in charge of data collection and proofread and revised the manuscript. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants and staff involved in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSteinhoff A, Ribeaud D, Kupferschmid S, Raible-Destan N, Quednow BB, Hepp U, Eisner M, Shanahan L. 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Analysis of the current situation of self-esteem level of secondary vocational students --Taking Jiangsu Agricultural and Animal Husbandry Science and Technology Vocational College as an example. \u003cem\u003eJournal of Jiamusi College of Education.\u003c/em\u003e 2017;\u003cem\u003e\u0026nbsp;\u003c/em\u003e12: 4-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Secondary Vocational Students, Time Attitudes, Positive Emotional Adjustment, Latent Profile Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6718305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6718305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Time attitude, as an affective dimension of time insight, is significantly related to adolescents’ adaptive and nonadaptive functioning. This study explored the types of time attitudes among Chinese secondary vocational students and assessed the relationship between different time attitude subgroupsand positive emotional adjustment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003e\u0026nbsp;A total of 2,310 secondary vocational students (M = 16.34, SD = 0.951; 41.7% female) participated in the study. Latent profile analysis was conducted using the Adolescent Time Attitude Scale, with positive emotional adjustment indicators analyzed as distal outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e \u0026nbsp;The analysis revealed three time attitude profiles: Positive, Ambivalent, and Negative. Secondary vocational students in the Positive profile had the highest levels of life satisfaction and positive affect, but the lowest level of self-esteem; those in the Ambivalent profile had the lowest levels of life satisfaction and positive emotions, but the highest level of self-esteem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e \u0026nbsp;Despite the limitations of the findings, they support existing research, enrich the theoretical framework in the field of time attitudes, and provide an empirical basis for the psychological adaptation of secondary vocational students.\u003c/p\u003e","manuscriptTitle":"The Latent Profiles of Time Attitudes and Positive Emotional Adjustment in Secondary Vocational Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 07:16:42","doi":"10.21203/rs.3.rs-6718305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-26T06:58:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T16:52:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71001219887852207691083048549135500559","date":"2025-06-08T22:55:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T14:24:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T15:28:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222947877427743968280417360654517650673","date":"2025-06-03T15:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279486188107724594117772925270308354546","date":"2025-06-03T15:15:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319145516460903732850315590204578334153","date":"2025-06-03T06:55:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T04:47:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-26T11:26:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-23T11:06:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-23T11:04:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-05-21T16:19:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88570f31-f8f2-4ab9-befd-0866b3eeb643","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:04:15+00:00","versionOfRecord":{"articleIdentity":"rs-6718305","link":"https://doi.org/10.1186/s40359-025-03928-x","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2026-01-20 15:57:13","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-06-05 07:16:42","video":"","vorDoi":"10.1186/s40359-025-03928-x","vorDoiUrl":"https://doi.org/10.1186/s40359-025-03928-x","workflowStages":[]},"version":"v1","identity":"rs-6718305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6718305","identity":"rs-6718305","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}