Latent class analysis of academic adjustment and mental health among Brazilian college students: association with depression and suicide ideation

Latent class analysis of academic adjustment and mental health among Brazilian college students: association with depression and suicide ideation | 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 Latent class analysis of academic adjustment and mental health among Brazilian college students: association with depression and suicide ideation Camila Siebert Altavini, Geilson Lima Santana, Laura Helena Andrade, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5397247/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Suicide is a leading cause of death among 15-29-year-olds. Effective prevention strategies are urgent, particularly for university students, where knowledge gaps regarding suicide-related factors hinders preventative efforts. The present study aimed to identify subgroups within Brazilian college students to examine the relationship of identified subgroups with suicidal ideation (SI) and depression. Methods Using academic and mental health indicator from a national survey of Brazilian college students, a latent class analysis was conducted to identify subgroups of students based on similar characteristics. Meaningful classes were subjected to logistic regression to identify potential associations with SI and depressive symptoms. Results Four distinct classes were identified, labeled as: “ordinary”, “psychologically distressed”, “dissatisfied”, and “binge drinkers”. The subgroups experiencing psychological distress and dissatisfaction were associated with a higher likelihood of presenting SI and depressive symptoms. Conclusion The impact of academic life on students' mental health must be closely monitored by the universities’ pedagogical and health services. Early identification of students in psychological distress is essential for appropriate referral to supportive services. Assessment of the relationship between suicide-related vulnerabilities is still very necessary to develop adequate prevention plans in educational settings. suicide young adult students depression Brazil latent class analysis Figures Figure 1 Figure 2 1. Introduction The increasing numbers of suicidal thoughts and behavior (STB) among youth is worrisome and represents a wakeup call to understand its epidemiology for developing preventive strategies [ 1 – 3 ]. During the adulting process youths take greater responsibility and independence than in adolescence. Additionally, college students are challenged with demanding academic routines. Previous studies found associations of STB with low social-connectedness, early-life adversities, mood and substance-use disorders, and school-related problems [ 4 – 9 ]. However, predictive value of individual risk factors is limited [ 10 ]. A person-centered approach that combines multiple factors could be more effective in depict vulnerable individuals [ 11 ]. We hypothesize that students can be clustered into subgroups based on academic adjustment and mental health indicators, and that subgroups could have distinct likelihood of reporting suicidal ideation, depressive symptoms, risky behaviors, and low academic achievement. To the best of our knowledge, no study has examined patterns of academic adjustment and mental health indicators while measuring their relationship to suicide ideation. This approach could help identify subgroups of students vulnerable to STB. Latent class analysis (LCA) allows to group individuals with similar characteristics within heterogeneous populations. By combining observable variables, LCA is a person-centered approach that categorizes complex real-world patterns that would otherwise be hard to depict [ 12 ]. In the present study, our primary aim is to identify subgroups of Brazilian college students from a nationally representative sample ( n = 12,245), according to academic adjustment and mental health indicators. Secondarily, we aim to analyze their association with suicidal ideation (SI), depressive symptoms (DS), risky behaviors, and low academic achievement. These findings could help in the early identification of psychologically distressed and at-risk students. 2. Methods 2.1. Sampling Using a cross-sectional design, this nationwide study investigates the use of alcohol, tobacco, and other drugs among college students, from 27 Brazilian state capitals [ 13 ]. A probabilistic and stratified sample from Higher Education Institutions (HEIs) was randomly selected and participants were recruited in a two-stage sampling process. Two HEIs of public- and private-funding from each state capital were selected, totaling 114 HEIs. The participating HEIs provided a list of all classroom-based undergraduate programs, from which classes were randomly selected. ‘Classes’ refers to groups of students enrolled in a particular subject during their undergraduate program. The data collection process was completed in 2009. Students from selected classes were invited to participate in the study. Considering the students in class during the survey, the response rate was 95.6%. All students regularly enrolled and that were present in the classroom during the questionnaire application were eligible. A total of 12,245 valid questionnaires were considered for analysis after excluding those that stated using the dummy drug “ Relevin ”, and those who did not answer the suicide ideation item. More details about the sampling process and statistical corrections can be found elsewhere [ 13 ]. 2.2. Instrument The students completed an anonymous, structured, and self-administered questionnaire with 98 closed questions focusing on drug use and related disorders, risky behavior, and psychiatric comorbidity, as well as sociodemographic and academic-life characteristics. Depressive symptoms were assessed using the Beck Depression Inventory-II (BDI-II), categorized to indicate the presence (score ≥ 11) or absence of depression [ 14 ]. The BDI-II is a validated self-reporting tool for assessing depressive symptoms in the Brazilian Portuguese-speaking population [ 14 ]. We used item #9 from the BDI-II to assess suicide ideation. The BDI-II item #9 asks the individual to choose, from the following statements, which one best describes their feelings during the last 15 days: (0) “ I don’t have thoughts of killing myself” ; (1) “ I have thoughts of killing myself, but I would not carry this out ”; (2) “ I would like to kill myself ”; (3) “ I would kill myself if I had the chance ”. We defined suicide ideation in a broader sense, including any cognition of killing oneself even if one would not carry it out [ 15 , 16 ]. Accordingly, item #9 was dichotomized into a “yes/no” variable to denote the presence or absence of suicide ideation, as adopted in previous literature [ 17 – 19 ]. Past-month psychological distress was investigated using the self-administered version of the K6 Scale, designed to discriminate cases of mental illness from non-cases [ 20 ]. For analytical purposes, the K6 score was dichotomized to indicate the presence (score ≥ 6) or absence of psychological distress [ 21 ]. Four questions from the Self-Report Questionnaire (SRQ) investigate past-month unusual experiences (e.g., hearing voices that others cannot, or suspicions of being followed). Participants were asked to answer “yes” or “no”: (1) “ Do you feel that someone, somehow, wants to hurt you? ”; (2) “ Are you someone much more important than most people think? ”; (3) “ Have you noticed any interference or other strange problems with your thinking ? ”; and (4) “ Do you hear voices you don’t know from where they come or that other people can’t hear? ”. The SRQ is recommended the by the World Health Organization (WHO) for quick detection and classification of community-dwelling individuals presenting persecutory symptoms, especially in developing countries [ 22 , 23 ]. Unusual psychotic-like experiences are associated with reduced psychological functioning, and poorer health status [ 24 , 25 ]. For analysis, the items were combined into a dichotomized variable to denote the presence or absence of such experiences. The structured questionnaire Alcohol, Smoking, and Substance Screening Test Involving (ASSIST) was used to collect substance use information. Our analysis focused on past-month binge drinking behavior [ 26 ] and general use of non-prescribed substances (inhalants, marijuana, cocaine, crack-cocaine, merla, amphetamines, anticholinergics, tranquilizers, opiate analgesics, sedatives, anabolic androgenic steroids, hallucinogens, ecstasy, and synthetic drugs). We combined the general non-prescribed drug items into a dichotomized item to indicate non-users and users. The ‘social activities’ variable was categorized considering if the student took part in none, one, or two or more activities that involved interaction with other persons. Further relevant topics were addressed in the survey tool by individual questions, such as academic performance in the last semester [“ In the past semester or academic year, you have: (1) Passed all subjects; (2) I resat for the exam but passed these subjects; (3) Pending subjects, but have not missed the year; (4) Repeated the year; (5) Other ], thoughts about dropping out the program or take a leave, [“ Regarding your undergraduate course (circle only one answer): (1) I’ve never thought of dropping out of the course or taking a leave of absence; (2) I’ve thought of dropping out of the course or taking a leave of absence; (3) I took a leave of absence once” ), current satisfaction with the chosen undergraduate course (“ Are you satisfied with the undergraduate course you have chosen?” ] and social activities when not in class [“ Except for your vacation period, which activities do you usually engage in when out of classroom? (1) I take part in student organizations (Academic Center/Fraternity) (2) I take part in academic projects guided by one or more professors. (3) I take part in physical or sporting activities. (4) I take part in inter-college sports competitions. (5) I study outside class hours. (6) I interact and spend time with my friends. (7) I watch TV or videos/DVDs. (8) I play video or PC games. (9) I use the Internet for fun (social networks, chat rooms, music, games and other types of online entertainment). (10) I send and receive emails. (11) I use Instant Messengers (e.g. MSN). (12) Other hobbies (reading books for pleasure, playing musical instruments, singing in choirs, drawing, painting, and other artistic activities). (13) Volunteer work (14) Paid job] . To better control the outliers’ effect, the variable ‘age’ was categorized into four categories: under 18 years old, 18–24, 25–34, and 35 or more years old. Economic strata were defined according to the Brazilian Association of Research Companies [ 27 ]. 2.3. Analys is A LCA method was used to group individuals based on shared features by identifying data covariance patterns of responses. The best-fitting model was selected, and posterior probabilities were saved into a new dataset for inclusion of covariates and outcomes [ 28 , 29 ]. Then, a logistic regression was conducted to examine relationships of identified latent classes with covariates and outcomes. Correction weights were applied to adjust for sampling error. We built the model based on previous findings [ 9 ]. The variable-specific entropy was considered for examining the quality of individual items, and those with near-zero values were removed from the model [ 30 ]. The latent-class model included indicators for: (a) academic adjustment: thoughts about dropping-out or taking a leave, and satisfaction with chosen course; (b) past-month mental health: psychological distress; unusual experiences; non-prescribed drugs’ use; binge drinking behavior. We then examined associations of identified latent classes with outcomes: suicide ideation, depressive symptoms, risky behavior, and academic achievement, adjusting for covariates: age, sex, economic status, HEI funding, employment, and social activity. Figure 1 represents the analysis’ path diagram. For facilitating the reading and interpretability of the results, we labeled the model as an “academic adjustment and mental health” model. INSERT FIG 1 NEAR HERE Figure 1. Caption The diagram illustrates the relationships between various factors such as academic adjustment, mental health, sociodemographic characteristics, and behaviors like drug use and binge drinking. The arrows indicate potential influences among these variables. The variables on the upper side of the figure represent the latent class indicators, the covariates are at the figure bottom, and the outcomes are on the right side of the figure, represented by the dark-gray boxes. Suicidal ideation provides important information for assessing and preventing suicide [ 31 ], as it is associated with future suicidal behavior [ 10 , 32 ]. Depressive symptoms were a separated outcome to prevent an overlap with SI, as both were assessed using BDI-II. Depression, identified as the most common mental disorder among college students [ 7 ], is a major suicide risk-factor [ 33 , 34 ]. Additionally, it also relates to psychological distress and challenges in academic life [ 8 ]. Academic achievement was also considered as outcome due to its potential impact on academic adjustment and mental health issues [ 35 ]. We tested different models with increasing numbers of classes to find the best fit for identifying patterns of academic adjustment and mental health. The model fit was assessed combining theoretical understanding of students’ mental health [ 7 – 9 ], information criteria – (e.g., Akaike Information Criteria -AIC, Bayesian Information Criteria -BIC, Sample-adjusted Bayesian Information Criteria - ABIC, and Consistent Akaike Information Criteria - CAIC), diagnostic criteria (e.g., entropy, class counts, Average Latent Class Posterior Probability), and the interpretability of the different models, as recommended [ 11 ]. Supplementary table S1 presents the results of information and diagnostic criteria for each k- class solution tested. The relationship between the identified classes with outcomes was examined using a logistic regression, adjusting for covariates. Highly skewed covariates were removed from the final logistic regression models, as recommended (Muthén, L. 2024, personal communication). STATA, version 15 [ 36 ] was used to run descriptive statistics, using the survey option ( 'svy' command) to adjust for sampling error and unequal probability of selection. For the target population, prevalence estimates and regression analyses are presented as weighted indicators. For the LCA and logistic regression analysis, we used the MPlus software, version 8.10 [ 37 ]. The logistic regression statistical tests were two-tailed with a significance level of 5%. 3. Results In our sample, 57.5% were women. The average age was 25 years (SE = 1.0). The majority had never been married (77.2%), identified as "white" (62.2%), and followed a religion (84.7%). Around half came from middle-to-high-income families (48.7%), and most students attended private-funded HEIs (77.7%). Nearly half reported past-month unusual experiences (49%), while 32.5% reported past-month psychological distress. Additionally, 25.7% presented DS, and 5.9% reported SI within the last two weeks. Regarding substance use, 24,4% of students indicated past-month use of non-prescribed drug, while the majority (58.5%) reported engaging in binge drinking behaviors. Detailed weighted proportions of sociodemographic characteristics is presented in previous analysis [ 18 ]. Table 1 provides the weighted proportions for the LCA model variables, covariates, and outcome. Table 1 Weighted proportions, of the college students’ academic and mental health characteristics, from the I Levantamento Nacional sobre o Uso de Álcool, Tabaco e Outras Drogas entre Universitários das 27 Capitais Brasileiras, 2009 Variable N (%*) 12,245 Academic characteristics Satisfaction with course No 997 (7.75) Yes 11,201 (92.25) Drop-out thoughts Never thought about dropping-out the program or leave of absence 7,866 (63.99) Have thought about dropping-out the program or leave of absence 3,721 (28.95) Have already taken a leave of absence 606 (7.06) Academic achievement Passed all courses 10,317 (86.06) Repeated the year 192 (2.47) Other 1,284 (11.47) Mental health and substance use Risky behavior No 6,463 (57.19) At least one 4,694 (42.81) Depressive symptoms No 8,344 (74.32) Yes 2,818 (25.68) Suicidal Ideation No 11,377 (94.08) Yes 868 (5.92) Psychological distress No 7,406 (67.55) Yes 3,718 (32.45) Unusual experiences None 5,937 (50.91) At least one 5,757 (49.09) Drug consumption No 9,912 (75.64) Yes 2,333 (24.36) Binge drinking No 4,071 (41.48) Yes 5,499 (58.52) * Weighted proportion of the full sample ᐞ Pearsons’ X², with Rao-Scott correction INSERT Table 1 NEAR HERE Figure 2 shows the conditional item probability for the 4-class LCA model. The x-axis represents the six items, while the y-axis shows the probability of endorsing a given item. More than half of the students were clustered in Class 2. These students exhibited overall satisfaction with the course, a probability of approximately 0.25 of considering dropping out of college or taking a leave of absence, less than 0.5 probability of reporting unusual experiences, zero endorsement for psychological distress, 0.27 probability of using drugs, and a higher probability of engaging in binge drinking behavior than other classes (0.74). Class 1 (26.3% of students) also reported overall satisfaction with the course, a slightly higher probability (0.36) of considering dropping out, higher probabilities of unusual experiences (0.74) and psychological distress (1.00), somewhat higher probability of overall drug use (0.33) and a slightly lower tendency for binge drinking behavior (0.60). Class 4 held 15.9% of students, characterized by the highest level of satisfaction among classes (0.98), the lowest probability of considering dropping out (0.11), lowest levels of unusual experiences (0.24), 0.20 probability of psychological distress, and no endorsement for drug use and binge drinking behavior. Finally, Class 3 was the least frequent (5.9% of students), characterized by the lowest levels of satisfaction with the course (0.08), the highest probability of considering dropping out (0.88), 0.57 probabilities of reporting unusual experiences, and 0.51 for psychological distress, 0.34 probability of engaging in drug use, and 0.63 probability of engaging in drinking behavior. INSERT FIGURE 2 NEAR HERE Figure 2. Caption : This graph illustrates the conditional item probability towards academic adjustment and mental health indicators among four classes: Class 1 - Distressed (26.3%), Class 2 - Binge-drinkers (51.9%), Class 3 - Dissatisfied (5.9%), and Class 4 - “Ordinary” (15.9%). The y-axis represents the item endorsement probability ranging from 0 to 1, while the x-axis lists factors such as satisfaction, thoughts of dropping out, unusual experiences, psychological distress, drug use, and binge drinking. Summarizing, we found four student classes: Class 2 (51.9%) represented by higher probabilities of engaging in binge drinking behavior, labeled as “Binge drinkers”; Class 1 (26.3%) with higher probability of unusual experiences and psychological distress, labeled as "Psychologically distressed”; Class 4 (15.9%) characterized by the “Ordinary” students, reporting high satisfaction with the course, low desire to drop-out, low levels of mental health problems, low drug use and binge drinking; and Class 3 (5.9%) representing the dissatisfied students, with higher likelihood of considering dropping out, labeled as “Dissatisfied”. Despite not being a central focus of this study, results indicated that ‘Binge drinkers’ and ‘Dissatisfied’ students were less likely to be women, compared with the “Ordinary” class. No other significant sociodemographic differences were found among classes (detailed results in Supplementary Table S2 ). Findings from logistic regression (Table 2 ) suggest that class membership may be linked to SI, DS, and risky behaviors, but not academic achievement. This indicates the presence of student subgroups with similar academic and mental health characteristics that may be associated with suicidal ideation and depressive symptoms. Table 2 Associations between class membership and outcomes. ‘Ordinary’ as reference OR [95%CI] Ordinary (Class 4) Binge drinkers (Class 2) Psychologically distressed (Class 1) Dissatisfied (Class 3) Suicidal Ideation (SI) 1.00 0.83 [0.14–4.93] 7.90 [1.94–32.22] 8.12 [2.27–29.02] Depressive symptoms (DS) 1.00 3.08 [0.54–17.52] 28.77 [6.43–128.75] 13.63 [3.47–53.59] Low academic achievement 1.00 1.87 [0.12–29.13] 1.64 [0.15–18.16] 3.26 [0.44–24.22] Risky behaviors 1.00 1.23 [0.70–2.16] 1.63 [0.90–2.94] 1.85 [1.00–3.40] INSERT Table 2 NEAR HERE As shown, the odds ratio for SI and DS was higher among psychologically distressed (SI: OR = 7.90, 95%CI = 1.94–32.22; DS: OR = 28.77, 95%CI = 6.43–128.75) and dissatisfied students (SI: OR = 8.12, 95%CI = 2.27–29.02; DS: OR = 13.63, 95%CI = 3.47–53.59). It is noteworthy, however, that the CIs are too large, which hinders the accuracy of estimates. Dissatisfied students also presented a higher likelihood of engaging in risky behaviors (OR = 1.85, 95%CI = 1.00–3.40). There was also a marginally significant association between psychologically distressed and risky behavior (OR = 1.63, 95%CI 0.90–2.94). There was no difference among classes regarding academic achievement. 4. Discussion We identified four subgroups of undergraduate students using LCA regarding academic adjustment and mental health. No similar study has been conducted in Brazil. The large sample size and sampling methods allow generalization of the results for this population without overestimating the results. A representative sample of Brazilian students could even yield generalizable results for Latin American countries and comparable upper-middle-income countries, once they share comparable sociodemographic characteristics. Both academic life and mental health aspects can be combined to identify students in different subgroups. In our study, the ‘dissatisfied’ class represented a minor proportion of students (5.9%), while at least one in four students was identified as ‘psychologically distressed’. The ‘Distressed’ students presented associations with SI and DS, whereas the ‘Dissatisfied’ was associated not only with SI and DS, but also with risky behavior. Clustering students into subgroups can facilitate early identification of vulnerable students, which can be used as a roadmap for institutional policy for mental health promotion and STB prevention. While the DS prevalence (25.7%) is in line with previous studies with Brazilian college students’ samples, the SI prevalence (5.9%) was lower [ 38 ]. Direct comparisons cannot be guaranteed, due to methodological differences. The limited sample sizes and the lack of standardized definitions, tools, and measures hinder comparisons and highlight the need for representative research among college students. Less than a third of studies about Brazilian college students used probabilistic sampling, and most were restricted to medicine students [ 38 ], which might lead to overestimations. While our initial findings provide valuable insights, wide 95% CIs indicate statistical imprecision and suggests considerable variability in the data, highlighting the importance of further research for a comprehensive understanding of mental health issues across Brazilian college students. Knowing populations’ specific stressors and protective factors can help in tailoring effective interventions. In the final step of our analysis, the logistic regression results indicated that ‘Psychologically distressed’ students might present higher prevalence of SI and DS. Dealing with the challenges of the adulting phase and the demanding academic routines becomes even harder when low social-connectedness, early-life adversities, mood and substance-use disorders, and school-related problems are included in the basket [ 4 – 6 , 8 , 34 ]. These associations between psychological distress, DS and SI serve as a warning call for stakeholders and reinforces the need for a broad policy for mental health promotion and suicide prevention strategies targeting college students. The model also identified a group of ‘Dissatisfied’ students associated with higher prevalence of SI, DS, and risky behaviors. This relationship between university satisfaction with SI, depression and risky behaviors could find favorable arguments in the consolidated association between health outcomes and life satisfaction. Life satisfaction is related with suicide-related outcomes [ 39 ]. It is plausible to hypothesize that students' satisfaction with their courses could influence their overall life satisfaction, as they spend most of time at university engaged in academic activities. This is especially true when social support is lacking, and pressures for academic and professional success are high. Considering these results, university staff should be vigilant in recognizing both explicit and implicit signs of student dissatisfaction so that they can identify vulnerable individuals at risk for not only DS and SI, but also risky behaviors that could result in serious and irreparable harm. Our results suggest that monitoring and tutoring specific college student subgroups, focusing on academic adjustment and mental health, can help identify at-risk students. Timely actions from university policymakers and managers are needed to address these challenges. Early identification of socio-academic vulnerabilities is crucial for preventive initiatives, supporting students with personal and social difficulties, and ensuring access to mental health services and academic support. Due to limited evidence, university staff often take ‘ad hoc’ measures based on observed demands [ 40 ]. One example of these measures is the implementation of a tutoring system where professors monitor a small group of students' academic and extracurricular challenges. These actions, akin to gatekeeping, enable early detection and referral to health services. However, many preventive actions in Brazilian universities are underreported and insufficiently evaluated. Is evident the need for more representative research to confirm vulnerabilities, using standardized outcomes. Prospective studies should be able to identify temporal relationships among these variables. Youth suicide leads to many potential years of life lost, highlighting the need for a better understanding of this phenomenon and development of effective evidence-based prevention strategies. 5. Limitations While LCA is an effective statistical technique, it has limitations. Individuals are assigned to classes based on their indicator variable scores. Additionally, since class assignment is probabilistic, the exact number or proportion of sample members in each class cannot be precisely determined. Furthermore, there is the risk of “naming fallacy” [ 11 ], as researchers name the identified classes, which may not always accurately reflect class membership. Additionally, our sample was recruited from state capitals. Thus, stated findings may only be generalizable to students living in urban areas. Although the questionnaire was built using reliable and validated instruments, recall errors and information biases cannot be discarded when using self-administered questionnaires. Additionally, respondents may be reluctant to disclose sensitive or embarrassing facts (e.g., drug use and suicide-related questions), responding bias should not be excluded. Since data collection - in 2009 - college students’ characteristics have changed, indicating an urgent update of studies with representative samples addressing students’ mental health. Finally, the cross-sectional design does not allow causal inference on associations with studied outcomes. 6. Conclusion This study enhances the understanding of STB among young adults by being the first to explore potential subgroups of college students based on mental health and academic factors combined. The findings show that students can be clustered into subgroups with similar traits of academic adjustment and mental health, which can be related to SI, depression, and risky behavior. These outcomes, along with social and individual vulnerabilities, are recognized risk factors for STB. Present results serve as a starting point for the development of in-campus interventions. By assessing students’ vulnerabilities and needs—while considering cultural, social, and institutional contexts—universities can effectively tailor policies and interventions. Using pre-defined indicators, institutions can identify key issues and address them with strategic actions. Students struggling with academic adjustment, dissatisfaction, or expressing drop-out desires should be considered as a group of potential vulnerability. Implementing preventive strategies, including facilitating access to healthcare and educational support, is crucial for supporting vulnerable students and promoting their well-being. Declarations Ethics statement All participants provided written informed consent before data collection. The Ethics Committee for the Analysis of Research Projects at the University of São Paulo Medical School approved the present study (protocol# 4.711.369). Conflicts of interest The authors have no conflicts of interest to disclose. Funding This work was supported by the National Anti-Drug Secretariat (SENAD), Brazilian Ministry of Health, supported data collection of this research. The agency has no further influence on the results reported herein, the decision to disseminate, the analytical strategy, and the contents of this article. Author Contribution C.S.A. wrote the main manuscript and ran statistical analyses to develop the methods and results sections of the manuscript. G.L.S. assisted with the statistical analyses and contributed to the development of the manuscript. A.G.A. provided the data analyzed in the manuscript and contributed to the development of the manuscript.W.Y-P. assisted with the statistical analyses, data management, and contributed to the development of the manuscript.All other others have either helped with either research design, data collection, data management, or data cleaning in addition to contributing to the development of the manuscript. Acknowledgement The authors thank Linda Muthén and the Mplus support service team for the support with statistical analysis and data management. This work was supported by the National Anti-Drug Secretariat (SENAD), Brazilian Ministry of Health, supported data collection of this research. References Alves FJO, Fialho E, de Araújo JAP, Naslund JA, Barreto ML, Patel V, Machado DB (2024) The rising trends of self-harm in Brazil: an ecological analysis of notifications, hospitalisations, and mortality between 2011 and 2022. 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Singapore Med J 31(5):457–462 Unterrassner L, Wyss TA, Wotruba D, Ajdacic-Gross V, Haker H, Rössler W (2017) Psychotic-Like Experiences at the Healthy End of the Psychosis Continuum. Front Psychol 8:775. https://doi.org/10.3389/fpsyg.2017.00775 Nuevo R, Chatterji S, Verdes E, Naidoo N, Arango C, Ayuso-Mateos JL (2012) The Continuum of Psychotic Symptoms in the General Population: A Cross-national Study. Schizophr Bull 38(3):475–485. https://doi.org/10.1093/schbul/sbq099 Centers for Disease Control and Prevention (CDC) (2019) What is Excessive Alcohol Use? Retrieved July 3, 2021, from https://www.cdc.gov/alcohol/onlinemedia/infographics/excessive-alcohol-use.html Associação Brasileira de Empresas de Pesquisa - ABEP. (n.d.). Critério de Classificação Econômica Brasil (2011) Retrieved July 3, 2021, from https://www.abep.org/criterio-brasil Asparouhov T, Muthén B (2014) Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus. Struct Equation Modeling: Multidisciplinary J 21(3):329–341. https://doi.org/10.1080/10705511.2014.915181 Nylund-Gibson K, Grimm RP, Masyn KE (2019) Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models. Struct Equation Modeling: Multidisciplinary J 26(6):967–985. https://doi.org/10.1080/10705511.2019.1590146 Asparouhov T, Muthén B (2018) Variable-Specific Entropy Contribution . Retrieved from http://www.statmodel.com/discussion/messages/13/1202.html?1511279738 Hawton K, Lascelles K, Pitman A, Gilbert S, Silverman M (2022) Assessment of suicide risk in mental health practice: shifting from prediction to therapeutic assessment, formulation, and risk management. Lancet Psychiatry. https://doi.org/10.1016/s2215-0366(22)00232-2 Large M, Corderoy A, McHugh C (2021) Is suicidal behaviour a stronger predictor of later suicide than suicidal ideation? A systematic review and meta-analysis. Australian New Z J Psychiatry 55(3):254–267. https://doi.org/10.1177/0004867420931161 Journal of Psychiatric Research , 95 , 253–259. https://doi.org/10.1016/j.jpsychires.2017.09.003 Casey SM, Varela A, Marriott JP, Coleman CM, Harlow BL (2022) The influence of diagnosed mental health conditions and symptoms of depression and/or anxiety on suicide ideation, plan, and attempt among college students: Findings from the Healthy Minds Study, 2018–2019. J Affect Disord 298(Pt A):464–471. https://doi.org/10.1016/j.jad.2021.11.006 BlackDeer AA, Wolf DAPS, Maguin E, Beeler-Stinn S (2021) Depression and anxiety among college students: Understanding the impact on grade average and differences in gender and ethnicity. J Am Coll Health 1–12. https://doi.org/10.1080/07448481.2021.1920954 StataCorp LLC (2017) Stata Statistical Software. StataCorp LLC, College Station, TX Muthén LK, Muthén BO (1998) Statistical Analysis With Latent Variables User’s Guide. Retrieved from www.StatModel.com Demenech LM, Oliveira AT, Neiva-Silva L, Dumith SC (2021) Prevalence of anxiety, depression and suicidal behaviors among Brazilian undergraduate students: A systematic review and meta-analysis. J Affect Disord 282:147–159. https://doi.org/10.1016/j.jad.2020.12.108 Goldman-Mellor SJ, Caspi A, Harrington H, Hogan S, Nada-Raja S, Poulton R, Moffitt TE (2014) Suicide Attempt in Young People: A Signal for Long-term Health Care and Social Needs. JAMA Psychiatry 71(2):119–127. https://doi.org/10.1001/jamapsychiatry.2013.2803 Reifschneider EDB, Altavini CS, de Beckmann CA (2022) A. International Handbook of Teaching and Learning in Health Promotion, Practices and Reflections from Around the World, 469–486. https://doi.org/10.1007/978-3-030-96005-6_29 Additional Declarations No competing interests reported. Supplementary Files 6AltaviniSupplementaryTableS1InformationCriteria2024.docx 6AltaviniSupplementaryTableS22024.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5397247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374601835,"identity":"4ee246f5-988c-45d9-a79f-62f2eb32f886","order_by":0,"name":"Camila Siebert Altavini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJCCAw8YGGTADAYDGwYGCQY2wloSGBh4eCBa0ojTwgDTAgSHCWvhbz+deCChxo7Hnv2M4eGCgvOJ26Ub2B5X4NEicSZ3w4GEY8k8PDw5BodnGNxO3DnnALvhGTxaDBhAWtiYgQ5LSzjMA9Sy4UYCm2QDPi38b4Fa/tXz8PA/A2k5R4QWCaAtiW2HeXgkkg8AtRwgrEXiBtCWxL7jPDw3HoO0JBtvuHOw3RCfFv7+3M0fPnyrlmPvT2z+zPPHTnbD7eZjD/FpwQYYSdUwCkbBKBgFowAdAAA1mVNEEmjobwAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade de Brasilia","correspondingAuthor":true,"prefix":"","firstName":"Camila","middleName":"Siebert","lastName":"Altavini","suffix":""},{"id":374601839,"identity":"9cdeff4a-4ee5-48fd-b4cb-239805a185d3","order_by":1,"name":"Geilson Lima Santana","email":"","orcid":"","institution":"Universidade de Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Geilson","middleName":"Lima","lastName":"Santana","suffix":""},{"id":374601841,"identity":"230ff22a-413c-4a01-a734-d02ac038928b","order_by":2,"name":"Laura Helena Andrade","email":"","orcid":"","institution":"Universidade de Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"Helena","lastName":"Andrade","suffix":""},{"id":374601842,"identity":"c61fe759-4279-47c6-b790-d7ac4eacd2f9","order_by":3,"name":"Lúcio Garcia Oliveira","email":"","orcid":"","institution":"Faculdade de Medicina do ABC","correspondingAuthor":false,"prefix":"","firstName":"Lúcio","middleName":"Garcia","lastName":"Oliveira","suffix":""},{"id":374601843,"identity":"47b9086c-57c6-4f62-b6b2-6e2e5efdcd0c","order_by":4,"name":"Arthur Guerra Andrade","email":"","orcid":"","institution":"Universidade de Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Arthur","middleName":"Guerra","lastName":"Andrade","suffix":""},{"id":374601844,"identity":"a8d9f79c-8d1b-4334-b38e-eda47ebfde1a","order_by":5,"name":"Clarice Gorenstein","email":"","orcid":"","institution":"Universidade de Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Clarice","middleName":"","lastName":"Gorenstein","suffix":""},{"id":374601845,"identity":"e3450a77-388c-4ceb-a668-ad07797f9386","order_by":6,"name":"Yuan-Pang Wang","email":"","orcid":"","institution":"Universidade de Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Yuan-Pang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-11-05 17:08:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5397247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5397247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69341793,"identity":"d4ee2a0e-64f3-4b99-ae2e-8f66a9b6b103","added_by":"auto","created_at":"2024-11-19 11:11:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":505297,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagram illustrates the relationships between various factors such as academic adjustment, mental health, sociodemographic characteristics, and behaviors like drug use and binge drinking. The arrows indicate potential influences among these variables. The variables on the upper side of the figure represent the latent class indicators, the covariates are at the figure bottom, and the outcomes are on the right side of the figure, represented by the dark-gray boxes.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397247/v1/a14c3a020bfcfbcfac25d5a1.jpg"},{"id":69343129,"identity":"5d2c1387-0a01-446f-a68f-f71aeb704ff9","added_by":"auto","created_at":"2024-11-19 11:27:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":721449,"visible":true,"origin":"","legend":"\u003cp\u003eThis graph illustrates the conditional item probability towards academic adjustment and mental health indicators among four classes: Class 1 - Distressed (26.3%), Class 2 - Binge-drinkers (51.9%), Class 3 - Dissatisfied (5.9%), and Class 4 - “Ordinary” (15.9%). The y-axis represents the item endorsement probability ranging from 0 to 1, while the x-axis lists factors such as satisfaction, thoughts of dropping out, unusual experiences, psychological distress, drug use, and binge drinking.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397247/v1/3f29434dac4adce560809031.jpg"},{"id":72825121,"identity":"7fa5f53e-db92-4f5a-a094-928fdd0632e6","added_by":"auto","created_at":"2025-01-02 14:32:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1836814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397247/v1/9c15f2e8-3d64-481e-8693-b8c71a3886d8.pdf"},{"id":69341792,"identity":"567c538c-5335-444e-b952-9c4db193c834","added_by":"auto","created_at":"2024-11-19 11:11:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18175,"visible":true,"origin":"","legend":"","description":"","filename":"6AltaviniSupplementaryTableS1InformationCriteria2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-5397247/v1/dfc599f3909ece3f3c6c999c.docx"},{"id":69341795,"identity":"880cb510-536c-4e9c-be97-79991cef1aa9","added_by":"auto","created_at":"2024-11-19 11:11:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17505,"visible":true,"origin":"","legend":"","description":"","filename":"6AltaviniSupplementaryTableS22024.docx","url":"https://assets-eu.researchsquare.com/files/rs-5397247/v1/abc9ad0ab517a4d7a407a823.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent class analysis of academic adjustment and mental health among Brazilian college students: association with depression and suicide ideation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe increasing numbers of suicidal thoughts and behavior (STB) among youth is worrisome and represents a wakeup call to understand its epidemiology for developing preventive strategies [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. During the adulting process youths take greater responsibility and independence than in adolescence. Additionally, college students are challenged with demanding academic routines. Previous studies found associations of STB with low social-connectedness, early-life adversities, mood and substance-use disorders, and school-related problems [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, predictive value of individual risk factors is limited [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A person-centered approach that combines multiple factors could be more effective in depict vulnerable individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We hypothesize that students can be clustered into subgroups based on academic adjustment and mental health indicators, and that subgroups could have distinct likelihood of reporting suicidal ideation, depressive symptoms, risky behaviors, and low academic achievement. To the best of our knowledge, no study has examined patterns of academic adjustment and mental health indicators while measuring their relationship to suicide ideation. This approach could help identify subgroups of students vulnerable to STB.\u003c/p\u003e \u003cp\u003eLatent class analysis (LCA) allows to group individuals with similar characteristics within heterogeneous populations. By combining observable variables, LCA is a person-centered approach that categorizes complex real-world patterns that would otherwise be hard to depict [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the present study, our primary aim is to identify subgroups of Brazilian college students from a nationally representative sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12,245), according to academic adjustment and mental health indicators. Secondarily, we aim to analyze their association with suicidal ideation (SI), depressive symptoms (DS), risky behaviors, and low academic achievement. These findings could help in the early identification of psychologically distressed and at-risk students.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sampling\u003c/h2\u003e \u003cp\u003eUsing a cross-sectional design, this nationwide study investigates the use of alcohol, tobacco, and other drugs among college students, from 27 Brazilian state capitals [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A probabilistic and stratified sample from Higher Education Institutions (HEIs) was randomly selected and participants were recruited in a two-stage sampling process. Two HEIs of public- and private-funding from each state capital were selected, totaling 114 HEIs. The participating HEIs provided a list of all classroom-based undergraduate programs, from which classes were randomly selected. \u0026lsquo;Classes\u0026rsquo; refers to groups of students enrolled in a particular subject during their undergraduate program. The data collection process was completed in 2009.\u003c/p\u003e \u003cp\u003eStudents from selected classes were invited to participate in the study. Considering the students in class during the survey, the response rate was 95.6%. All students regularly enrolled and that were present in the classroom during the questionnaire application were eligible. A total of 12,245 valid questionnaires were considered for analysis after excluding those that stated using the dummy drug \u0026ldquo;\u003cem\u003eRelevin\u003c/em\u003e\u0026rdquo;, and those who did not answer the suicide ideation item. More details about the sampling process and statistical corrections can be found elsewhere [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Instrument\u003c/h2\u003e \u003cp\u003eThe students completed an anonymous, structured, and self-administered questionnaire with 98 closed questions focusing on drug use and related disorders, risky behavior, and psychiatric comorbidity, as well as sociodemographic and academic-life characteristics.\u003c/p\u003e \u003cp\u003eDepressive symptoms were assessed using the Beck Depression Inventory-II (BDI-II), categorized to indicate the presence (score\u0026thinsp;\u0026ge;\u0026thinsp;11) or absence of depression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The BDI-II is a validated self-reporting tool for assessing depressive symptoms in the Brazilian Portuguese-speaking population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We used item #9 from the BDI-II to assess suicide ideation. The BDI-II item #9 asks the individual to choose, from the following statements, which one best describes their feelings during the last 15 days: (0) \u0026ldquo;\u003cem\u003eI don\u0026rsquo;t have thoughts of killing myself\u0026rdquo;\u003c/em\u003e; (1) \u0026ldquo;\u003cem\u003eI have thoughts of killing myself, but I would not carry this out\u003c/em\u003e\u0026rdquo;; (2) \u0026ldquo;\u003cem\u003eI would like to kill myself\u003c/em\u003e\u0026rdquo;; (3) \u0026ldquo;\u003cem\u003eI would kill myself if I had the chance\u003c/em\u003e\u0026rdquo;. We defined suicide ideation in a broader sense, including any cognition of killing oneself even if one would not carry it out [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Accordingly, item #9 was dichotomized into a \u0026ldquo;yes/no\u0026rdquo; variable to denote the presence or absence of suicide ideation, as adopted in previous literature [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePast-month psychological distress was investigated using the self-administered version of the K6 Scale, designed to discriminate cases of mental illness from non-cases [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For analytical purposes, the K6 score was dichotomized to indicate the presence (score\u0026thinsp;\u0026ge;\u0026thinsp;6) or absence of psychological distress [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFour questions from the Self-Report Questionnaire (SRQ) investigate past-month unusual experiences (e.g., hearing voices that others cannot, or suspicions of being followed). Participants were asked to answer \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;: (1) \u0026ldquo;\u003cem\u003eDo you feel that someone, somehow, wants to hurt you?\u003c/em\u003e\u0026rdquo;; (2) \u0026ldquo;\u003cem\u003eAre you someone much more\u003c/em\u003e important \u003cem\u003ethan most people think?\u003c/em\u003e\u0026rdquo;; (3) \u0026ldquo;\u003cem\u003eHave you noticed any interference or other strange problems with your\u003c/em\u003e thinking\u003cem\u003e?\u003c/em\u003e\u0026rdquo;; and (4) \u0026ldquo;\u003cem\u003eDo you hear voices you don\u0026rsquo;t know from where they come or that other people can\u0026rsquo;t hear?\u003c/em\u003e\u0026rdquo;. The SRQ is recommended the by the World Health Organization (WHO) for quick detection and classification of community-dwelling individuals presenting persecutory symptoms, especially in developing countries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Unusual psychotic-like experiences are associated with reduced psychological functioning, and poorer health status [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For analysis, the items were combined into a dichotomized variable to denote the presence or absence of such experiences.\u003c/p\u003e \u003cp\u003eThe structured questionnaire Alcohol, Smoking, and Substance Screening Test Involving (ASSIST) was used to collect substance use information. Our analysis focused on past-month binge drinking behavior [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and general use of non-prescribed substances (inhalants, marijuana, cocaine, crack-cocaine, merla, amphetamines, anticholinergics, tranquilizers, opiate analgesics, sedatives, anabolic androgenic steroids, hallucinogens, ecstasy, and synthetic drugs). We combined the general non-prescribed drug items into a dichotomized item to indicate non-users and users. The \u0026lsquo;social activities\u0026rsquo; variable was categorized considering if the student took part in none, one, or two or more activities that involved interaction with other persons.\u003c/p\u003e \u003cp\u003eFurther relevant topics were addressed in the survey tool by individual questions, such as academic performance in the last semester [\u0026ldquo;\u003cem\u003eIn the past semester or academic year, you have: (1) Passed all subjects; (2) I resat for the exam but passed these subjects; (3) Pending subjects, but have not missed the year; (4) Repeated the year; (5) Other\u003c/em\u003e], thoughts about dropping out the program or take a leave, [\u0026ldquo;\u003cem\u003eRegarding your undergraduate course (circle only one answer): (1) I\u0026rsquo;ve never thought of dropping out of the course or taking a leave of absence; (2) I\u0026rsquo;ve thought of dropping out of the course or taking a leave of absence; (3) I took a leave of absence once\u0026rdquo;\u003c/em\u003e), current satisfaction with the chosen undergraduate course (\u0026ldquo;\u003cem\u003eAre you satisfied with the undergraduate course you have chosen?\u0026rdquo;\u003c/em\u003e] and social activities when not in class [\u0026ldquo;\u003cem\u003eExcept for your vacation period, which activities do you usually engage in when out of classroom? (1) I take part in student organizations (Academic Center/Fraternity) (2) I take part in academic projects guided by one or more professors. (3) I take part in physical or sporting activities. (4) I take part in inter-college sports competitions. (5) I study outside class hours. (6) I interact and spend time with my friends. (7) I watch TV or videos/DVDs. (8) I play video or PC games. (9) I use the Internet for fun (social networks, chat rooms, music, games and other types of online entertainment). (10) I send and receive emails. (11) I use Instant Messengers (e.g. MSN). (12) Other hobbies (reading books for pleasure, playing musical instruments, singing in choirs, drawing, painting, and other artistic activities). (13) Volunteer work (14) Paid job]\u003c/em\u003e. To better control the outliers\u0026rsquo; effect, the variable \u0026lsquo;age\u0026rsquo; was categorized into four categories: under 18 years old, 18\u0026ndash;24, 25\u0026ndash;34, and 35 or more years old. Economic strata were defined according to the Brazilian Association of Research Companies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u003cem\u003eAnalys\u003c/em\u003eis\u003c/h2\u003e \u003cp\u003eA LCA method was used to group individuals based on shared features by identifying data covariance patterns of responses. The best-fitting model was selected, and posterior probabilities were saved into a new dataset for inclusion of covariates and outcomes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Then, a logistic regression was conducted to examine relationships of identified latent classes with covariates and outcomes. Correction weights were applied to adjust for sampling error.\u003c/p\u003e \u003cp\u003eWe built the model based on previous findings [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The variable-specific entropy was considered for examining the quality of individual items, and those with near-zero values were removed from the model [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The latent-class model included indicators for: (a) academic adjustment: thoughts about dropping-out or taking a leave, and satisfaction with chosen course; (b) past-month mental health: psychological distress; unusual experiences; non-prescribed drugs\u0026rsquo; use; binge drinking behavior. We then examined associations of identified latent classes with outcomes: suicide ideation, depressive symptoms, risky behavior, and academic achievement, adjusting for covariates: age, sex, economic status, HEI funding, employment, and social activity. Figure\u0026nbsp;1 represents the analysis\u0026rsquo; path diagram. For facilitating the reading and interpretability of the results, we labeled the model as an \u0026ldquo;academic adjustment and mental health\u0026rdquo; model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT FIG 1 NEAR HERE\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure\u0026nbsp;1. Caption\u003c/strong\u003e \u003cp\u003eThe diagram illustrates the relationships between various factors such as academic adjustment, mental health, sociodemographic characteristics, and behaviors like drug use and binge drinking. The arrows indicate potential influences among these variables. The variables on the upper side of the figure represent the latent class indicators, the covariates are at the figure bottom, and the outcomes are on the right side of the figure, represented by the dark-gray boxes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSuicidal ideation provides important information for assessing and preventing suicide [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], as it is associated with future suicidal behavior [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Depressive symptoms were a separated outcome to prevent an overlap with SI, as both were assessed using BDI-II. Depression, identified as the most common mental disorder among college students [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], is a major suicide risk-factor [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, it also relates to psychological distress and challenges in academic life [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Academic achievement was also considered as outcome due to its potential impact on academic adjustment and mental health issues [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe tested different models with increasing numbers of classes to find the best fit for identifying patterns of academic adjustment and mental health. The model fit was assessed combining theoretical understanding of students\u0026rsquo; mental health [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], information criteria \u0026ndash; (e.g., Akaike Information Criteria -AIC, Bayesian Information Criteria -BIC, Sample-adjusted Bayesian Information Criteria - ABIC, and Consistent Akaike Information Criteria - CAIC), diagnostic criteria (e.g., entropy, class counts, Average Latent Class Posterior Probability), and the interpretability of the different models, as recommended [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cb\u003eSupplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e presents the results of information and diagnostic criteria for each \u003cem\u003ek-\u003c/em\u003eclass solution tested.\u003c/p\u003e \u003cp\u003eThe relationship between the identified classes with outcomes was examined using a logistic regression, adjusting for covariates. Highly skewed covariates were removed from the final logistic regression models, as recommended (Muth\u0026eacute;n, L. 2024, personal communication).\u003c/p\u003e \u003cp\u003eSTATA, version 15 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was used to run descriptive statistics, using the survey option (\u003cem\u003e'svy'\u003c/em\u003e command) to adjust for sampling error and unequal probability of selection. For the target population, prevalence estimates and regression analyses are presented as weighted indicators. For the LCA and logistic regression analysis, we used the MPlus software, version 8.10 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The logistic regression statistical tests were two-tailed with a significance level of 5%.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn our sample, 57.5% were women. The average age was 25 years (SE\u0026thinsp;=\u0026thinsp;1.0). The majority had never been married (77.2%), identified as \"white\" (62.2%), and followed a religion (84.7%). Around half came from middle-to-high-income families (48.7%), and most students attended private-funded HEIs (77.7%). Nearly half reported past-month unusual experiences (49%), while 32.5% reported past-month psychological distress. Additionally, 25.7% presented DS, and 5.9% reported SI within the last two weeks. Regarding substance use, 24,4% of students indicated past-month use of non-prescribed drug, while the majority (58.5%) reported engaging in binge drinking behaviors. Detailed weighted proportions of sociodemographic characteristics is presented in previous analysis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the weighted proportions for the LCA model variables, covariates, and outcome.\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\u003eWeighted proportions, of the college students\u0026rsquo; academic and mental health characteristics, from the I Levantamento Nacional sobre o Uso de \u0026Aacute;lcool, Tabaco e Outras Drogas entre Universit\u0026aacute;rios das 27 Capitais Brasileiras, 2009\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%*)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,245\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAcademic characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSatisfaction with course\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e997 (7.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,201 (92.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrop-out thoughts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever thought about dropping-out the program or leave of absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,866 (63.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave thought about dropping-out the program or leave of absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,721 (28.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave already taken a leave of absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e606 (7.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcademic achievement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassed all courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,317 (86.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated the year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (2.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,284 (11.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMental health and substance use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisky behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,463 (57.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,694 (42.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepressive symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,344 (74.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,818 (25.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuicidal Ideation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,377 (94.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e868 (5.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological distress\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,406 (67.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,718 (32.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnusual experiences\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,937 (50.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,757 (49.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrug consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,912 (75.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,333 (24.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBinge drinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,071 (41.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,499 (58.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e* Weighted proportion of the full sample\u003c/p\u003e \u003cp\u003eᐞ Pearsons\u0026rsquo; X\u0026sup2;, with Rao-Scott correction\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\u003e \u003cb\u003eINSERT\u003c/b\u003e Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eNEAR HERE\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e shows the conditional item probability for the 4-class LCA model. The x-axis represents the six items, while the y-axis shows the probability of endorsing a given item. More than half of the students were clustered in Class 2. These students exhibited overall satisfaction with the course, a probability of approximately 0.25 of considering dropping out of college or taking a leave of absence, less than 0.5 probability of reporting unusual experiences, zero endorsement for psychological distress, 0.27 probability of using drugs, and a higher probability of engaging in binge drinking behavior than other classes (0.74). Class 1 (26.3% of students) also reported overall satisfaction with the course, a slightly higher probability (0.36) of considering dropping out, higher probabilities of unusual experiences (0.74) and psychological distress (1.00), somewhat higher probability of overall drug use (0.33) and a slightly lower tendency for binge drinking behavior (0.60). Class 4 held 15.9% of students, characterized by the highest level of satisfaction among classes (0.98), the lowest probability of considering dropping out (0.11), lowest levels of unusual experiences (0.24), 0.20 probability of psychological distress, and no endorsement for drug use and binge drinking behavior. Finally, Class 3 was the least frequent (5.9% of students), characterized by the lowest levels of satisfaction with the course (0.08), the highest probability of considering dropping out (0.88), 0.57 probabilities of reporting unusual experiences, and 0.51 for psychological distress, 0.34 probability of engaging in drug use, and 0.63 probability of engaging in drinking behavior.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT FIGURE 2 NEAR HERE\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2. Caption\u003c/b\u003e: This graph illustrates the conditional item probability towards academic adjustment and mental health indicators among four classes: Class 1 - Distressed (26.3%), Class 2 - Binge-drinkers (51.9%), Class 3 - Dissatisfied (5.9%), and Class 4 - \u0026ldquo;Ordinary\u0026rdquo; (15.9%). The y-axis represents the item endorsement probability ranging from 0 to 1, while the x-axis lists factors such as satisfaction, thoughts of dropping out, unusual experiences, psychological distress, drug use, and binge drinking.\u003c/p\u003e \u003cp\u003eSummarizing, we found four student classes: Class 2 (51.9%) represented by higher probabilities of engaging in binge drinking behavior, labeled as \u0026ldquo;Binge drinkers\u0026rdquo;; Class 1 (26.3%) with higher probability of unusual experiences and psychological distress, labeled as \"Psychologically distressed\u0026rdquo;; Class 4 (15.9%) characterized by the \u0026ldquo;Ordinary\u0026rdquo; students, reporting high satisfaction with the course, low desire to drop-out, low levels of mental health problems, low drug use and binge drinking; and Class 3 (5.9%) representing the dissatisfied students, with higher likelihood of considering dropping out, labeled as \u0026ldquo;Dissatisfied\u0026rdquo;. Despite not being a central focus of this study, results indicated that \u0026lsquo;Binge drinkers\u0026rsquo; and \u0026lsquo;Dissatisfied\u0026rsquo; students were less likely to be women, compared with the \u0026ldquo;Ordinary\u0026rdquo; class. No other significant sociodemographic differences were found among classes (detailed results in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFindings from logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) suggest that class membership may be linked to SI, DS, and risky behaviors, but not academic achievement. This indicates the presence of student subgroups with similar academic and mental health characteristics that may be associated with suicidal ideation and depressive symptoms.\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\u003eAssociations between class membership and outcomes. \u0026lsquo;Ordinary\u0026rsquo; as reference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOR [95%CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrdinary\u003c/p\u003e \u003cp\u003e(Class 4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinge drinkers\u003c/p\u003e \u003cp\u003e(Class 2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychologically distressed\u003c/p\u003e \u003cp\u003e(Class 1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDissatisfied\u003c/p\u003e \u003cp\u003e(Class 3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuicidal Ideation (SI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 [0.14\u0026ndash;4.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.90 [1.94\u0026ndash;32.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.12 [2.27\u0026ndash;29.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepressive symptoms (DS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08 [0.54\u0026ndash;17.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.77 [6.43\u0026ndash;128.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.63 [3.47\u0026ndash;53.59]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow academic achievement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87 [0.12\u0026ndash;29.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64 [0.15\u0026ndash;18.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.26 [0.44\u0026ndash;24.22]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisky behaviors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 [0.70\u0026ndash;2.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63 [0.90\u0026ndash;2.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85 [1.00\u0026ndash;3.40]\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\u003e \u003cb\u003eINSERT\u003c/b\u003e Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eNEAR HERE\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs shown, the odds ratio for SI and DS was higher among psychologically distressed (SI: OR\u0026thinsp;=\u0026thinsp;7.90, 95%CI\u0026thinsp;=\u0026thinsp;1.94\u0026ndash;32.22; DS: OR\u0026thinsp;=\u0026thinsp;28.77, 95%CI\u0026thinsp;=\u0026thinsp;6.43\u0026ndash;128.75) and dissatisfied students (SI: OR\u0026thinsp;=\u0026thinsp;8.12, 95%CI\u0026thinsp;=\u0026thinsp;2.27\u0026ndash;29.02; DS: OR\u0026thinsp;=\u0026thinsp;13.63, 95%CI\u0026thinsp;=\u0026thinsp;3.47\u0026ndash;53.59). It is noteworthy, however, that the CIs are too large, which hinders the accuracy of estimates. Dissatisfied students also presented a higher likelihood of engaging in risky behaviors (OR\u0026thinsp;=\u0026thinsp;1.85, 95%CI\u0026thinsp;=\u0026thinsp;1.00\u0026ndash;3.40). There was also a marginally significant association between psychologically distressed and risky behavior (OR\u0026thinsp;=\u0026thinsp;1.63, 95%CI 0.90\u0026ndash;2.94). There was no difference among classes regarding academic achievement.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe identified four subgroups of undergraduate students using LCA regarding academic adjustment and mental health. No similar study has been conducted in Brazil. The large sample size and sampling methods allow generalization of the results for this population without overestimating the results. A representative sample of Brazilian students could even yield generalizable results for Latin American countries and comparable upper-middle-income countries, once they share comparable sociodemographic characteristics. Both academic life and mental health aspects can be combined to identify students in different subgroups. In our study, the \u0026lsquo;dissatisfied\u0026rsquo; class represented a minor proportion of students (5.9%), while at least one in four students was identified as \u0026lsquo;psychologically distressed\u0026rsquo;. The \u0026lsquo;Distressed\u0026rsquo; students presented associations with SI and DS, whereas the \u0026lsquo;Dissatisfied\u0026rsquo; was associated not only with SI and DS, but also with risky behavior. Clustering students into subgroups can facilitate early identification of vulnerable students, which can be used as a roadmap for institutional policy for mental health promotion and STB prevention.\u003c/p\u003e \u003cp\u003eWhile the DS prevalence (25.7%) is in line with previous studies with Brazilian college students\u0026rsquo; samples, the SI prevalence (5.9%) was lower [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Direct comparisons cannot be guaranteed, due to methodological differences. The limited sample sizes and the lack of standardized definitions, tools, and measures hinder comparisons and highlight the need for representative research among college students. Less than a third of studies about Brazilian college students used probabilistic sampling, and most were restricted to medicine students [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which might lead to overestimations. While our initial findings provide valuable insights, wide 95% CIs indicate statistical imprecision and suggests considerable variability in the data, highlighting the importance of further research for a comprehensive understanding of mental health issues across Brazilian college students. Knowing populations\u0026rsquo; specific stressors and protective factors can help in tailoring effective interventions.\u003c/p\u003e \u003cp\u003eIn the final step of our analysis, the logistic regression results indicated that \u0026lsquo;Psychologically distressed\u0026rsquo; students might present higher prevalence of SI and DS. Dealing with the challenges of the adulting phase and the demanding academic routines becomes even harder when low social-connectedness, early-life adversities, mood and substance-use disorders, and school-related problems are included in the basket [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These associations between psychological distress, DS and SI serve as a warning call for stakeholders and reinforces the need for a broad policy for mental health promotion and suicide prevention strategies targeting college students.\u003c/p\u003e \u003cp\u003eThe model also identified a group of \u0026lsquo;Dissatisfied\u0026rsquo; students associated with higher prevalence of SI, DS, and risky behaviors. This relationship between university satisfaction with SI, depression and risky behaviors could find favorable arguments in the consolidated association between health outcomes and life satisfaction. Life satisfaction is related with suicide-related outcomes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It is plausible to hypothesize that students' satisfaction with their courses could influence their overall life satisfaction, as they spend most of time at university engaged in academic activities. This is especially true when social support is lacking, and pressures for academic and professional success are high. Considering these results, university staff should be vigilant in recognizing both explicit and implicit signs of student dissatisfaction so that they can identify vulnerable individuals at risk for not only DS and SI, but also risky behaviors that could result in serious and irreparable harm.\u003c/p\u003e \u003cp\u003eOur results suggest that monitoring and tutoring specific college student subgroups, focusing on academic adjustment and mental health, can help identify at-risk students. Timely actions from university policymakers and managers are needed to address these challenges. Early identification of socio-academic vulnerabilities is crucial for preventive initiatives, supporting students with personal and social difficulties, and ensuring access to mental health services and academic support. Due to limited evidence, university staff often take \u0026lsquo;ad hoc\u0026rsquo; measures based on observed demands [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. One example of these measures is the implementation of a tutoring system where professors monitor a small group of students' academic and extracurricular challenges. These actions, akin to gatekeeping, enable early detection and referral to health services. However, many preventive actions in Brazilian universities are underreported and insufficiently evaluated.\u003c/p\u003e \u003cp\u003eIs evident the need for more representative research to confirm vulnerabilities, using standardized outcomes. Prospective studies should be able to identify temporal relationships among these variables. Youth suicide leads to many potential years of life lost, highlighting the need for a better understanding of this phenomenon and development of effective evidence-based prevention strategies.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eWhile LCA is an effective statistical technique, it has limitations. Individuals are assigned to classes based on their indicator variable scores. Additionally, since class assignment is probabilistic, the exact number or proportion of sample members in each class cannot be precisely determined. Furthermore, there is the risk of \u0026ldquo;naming fallacy\u0026rdquo; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], as researchers name the identified classes, which may not always accurately reflect class membership. Additionally, our sample was recruited from state capitals. Thus, stated findings may only be generalizable to students living in urban areas. Although the questionnaire was built using reliable and validated instruments, recall errors and information biases cannot be discarded when using self-administered questionnaires. Additionally, respondents may be reluctant to disclose sensitive or embarrassing facts (e.g., drug use and suicide-related questions), responding bias should not be excluded. Since data collection - in 2009 - college students\u0026rsquo; characteristics have changed, indicating an urgent update of studies with representative samples addressing students\u0026rsquo; mental health. Finally, the cross-sectional design does not allow causal inference on associations with studied outcomes.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study enhances the understanding of STB among young adults by being the first to explore potential subgroups of college students based on mental health and academic factors combined. The findings show that students can be clustered into subgroups with similar traits of academic adjustment and mental health, which can be related to SI, depression, and risky behavior. These outcomes, along with social and individual vulnerabilities, are recognized risk factors for STB. Present results serve as a starting point for the development of in-campus interventions. By assessing students\u0026rsquo; vulnerabilities and needs\u0026mdash;while considering cultural, social, and institutional contexts\u0026mdash;universities can effectively tailor policies and interventions. Using pre-defined indicators, institutions can identify key issues and address them with strategic actions. Students struggling with academic adjustment, dissatisfaction, or expressing drop-out desires should be considered as a group of potential vulnerability. Implementing preventive strategies, including facilitating access to healthcare and educational support, is crucial for supporting vulnerable students and promoting their well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent before data collection. The Ethics Committee for the Analysis of Research Projects at the University of S\u0026atilde;o Paulo Medical School approved the present study (protocol# 4.711.369).\u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Anti-Drug Secretariat (SENAD), Brazilian Ministry of Health, supported data collection of this research. The agency has no further influence on the results reported herein, the decision to disseminate, the analytical strategy, and the contents of this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.S.A. wrote the main manuscript and ran statistical analyses to develop the methods and results sections of the manuscript. G.L.S. assisted with the statistical analyses and contributed to the development of the manuscript. A.G.A. provided the data analyzed in the manuscript and contributed to the development of the manuscript.W.Y-P. assisted with the statistical analyses, data management, and contributed to the development of the manuscript.All other others have either helped with either research design, data collection, data management, or data cleaning in addition to contributing to the development of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Linda Muth\u0026eacute;n and the Mplus support service team for the support with statistical analysis and data management. This work was supported by the National Anti-Drug Secretariat (SENAD), Brazilian Ministry of Health, supported data collection of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlves FJO, Fialho E, de Ara\u0026uacute;jo JAP, Naslund JA, Barreto ML, Patel V, Machado DB (2024) The rising trends of self-harm in Brazil: an ecological analysis of notifications, hospitalisations, and mortality between 2011 and 2022. \u003cem\u003eThe Lancet Regional Health - Americas\u003c/em\u003e, 100691. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lana.2024.100691\u003c/span\u003e\u003cspan address=\"10.1016/j.lana.2024.100691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeter AR, Van, Knowles EA, Mintz EH (2022) Systematic Review and Meta-Analysis: International Prevalence of Suicidal Ideation and Attempt in Youth. 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International Handbook of Teaching and Learning in Health Promotion, Practices and Reflections from Around the World, 469\u0026ndash;486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-96005-6_29\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-96005-6_29\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"suicide, young adult, students, depression, Brazil, latent class analysis","lastPublishedDoi":"10.21203/rs.3.rs-5397247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5397247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSuicide is a leading cause of death among 15-29-year-olds. Effective prevention strategies are urgent, particularly for university students, where knowledge gaps regarding suicide-related factors hinders preventative efforts. The present study aimed to identify subgroups within Brazilian college students to examine the relationship of identified subgroups with suicidal ideation (SI) and depression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing academic and mental health indicator from a national survey of Brazilian college students, a latent class analysis was conducted to identify subgroups of students based on similar characteristics. Meaningful classes were subjected to logistic regression to identify potential associations with SI and depressive symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour distinct classes were identified, labeled as: \u0026ldquo;ordinary\u0026rdquo;, \u0026ldquo;psychologically distressed\u0026rdquo;, \u0026ldquo;dissatisfied\u0026rdquo;, and \u0026ldquo;binge drinkers\u0026rdquo;. The subgroups experiencing psychological distress and dissatisfaction were associated with a higher likelihood of presenting SI and depressive symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe impact of academic life on students' mental health must be closely monitored by the universities\u0026rsquo; pedagogical and health services. Early identification of students in psychological distress is essential for appropriate referral to supportive services. Assessment of the relationship between suicide-related vulnerabilities is still very necessary to develop adequate prevention plans in educational settings.\u003c/p\u003e","manuscriptTitle":"Latent class analysis of academic adjustment and mental health among Brazilian college students: association with depression and suicide ideation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 11:11:07","doi":"10.21203/rs.3.rs-5397247/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b2eaffb-4238-44c0-a1ad-11f66206887a","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T14:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 11:11:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5397247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5397247","identity":"rs-5397247","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}