In recent decades, university student dropout causes and implications have been studied. However, its risk factors have not yet been established (Bardach et al., 2020), even less so in the Chilean higher education system and with students with a high prevalence of symptoms connected to anxiety, depression, and post-pandemic stress (Martínez-Líbano et al., 2023). Consequently, this study aimed to determine the psychosocial variables influencing the risk of dropping out of the higher education system in a sample of post-pandemic Chilean university students.

Dropout can be understood as the definitive termination of the higher education system without obtaining an academic degree (Heublein, 2014). It can also be considered students dropping out of higher education before completing their academic program (Croninger and Lee, 2001). More specific definitions explain that dropping out can be understood as “a student who is enrolled in public school, does not return to public school the following fall, is not expelled, and does not graduate, receive a General Educational Development (GED) certificate, continue school outside the public school system, begin college, or die” (Texas Education Agency, 2023). Therefore, a standard definition encompassing different educational systems, public and private, and at different educational levels would define dropout as the voluntary act of a student not returning to the educational system before completing their academic program.

In 2020, more than 300,000 university students worldwide dropped out of school. The university dropout rate reached 18.6% in 2020, an indicator six points higher than that recorded in 2019, equivalent to 12% (Vega et al., 2022). The dropout rate in some studies in the first and second year reaches 44.8, 15.9% in the third year, and 15.9% in the fourth year (Alban and Mauricio, 2019). A university dropout rate of over 40% in Latin America has been observed (Fonseca-Grandón, 2018). Attrition results from the interaction of factors that negatively influence students not completing their university studies (Casanova et al., 2023).

University dropouts in Chile present a series of indicators and problems that reflect the complexity of this phenomenon. In some studies in Chile that account for that in regular undergraduate programs, the dropout rate before the social outbreak in the first semester of 2019 was 3.97%, a reduction of 0.14% compared to the second semester of 2018. However, during the second semester of 2019, the attrition rate increased by 0.55%, ending the year at 4.52% (Tercera, 2023). A crucial aspect is the difference in dropout rates between public and private universities, which is higher in the former (Gutiérrez et al., 2021)). This indicator suggests challenges inherent to the management and resources of state institutions. In addition, dropout affects students and their families and significantly impacts the educational system and society in general, as it limits the development of qualified human capital and increases inequity in access to higher education.

Several key elements have been identified regarding the specific factors of university dropout in Chile. Academic performance is a cross-cutting factor, particularly critical during the first year of studies (Gallegos et al., 2018). Other factors include region of origin, age, year of entry, and education funding, which play an essential role in student retention (Barrios, 2011). These results underscore the importance of effective university management and well-targeted funding policies to reduce dropout and promote equity in higher education.

The decision to drop out of the current curriculum can best be understood as a protracted, complex, and multifaceted process in which different influencing factors accumulate to create a constellation of problems that makes dropping out seem inevitable (Bardach et al., 2020). Specifically, researchers often distinguish between students’ structural and individual characteristics (Heublein, 2014).

Possible college dropout factors include incompatibility between work and study time (Quinn, 2004; Behr et al., 2021), economic factors (Casanova et al., 2021; Aina et al., 2022), personal factors (Kemper et al., 2020; Tsai et al., 2020), vocational orientation (Schnettler et al., 2020; Casanova et al., 2021), motivational factors (Jiang et al., 2020; Kryshko et al., 2020), unmet expectations (Marôco et al., 2020; Aina et al., 2022), course quality (Perez et al., 2018; Del Bonifro et al., 2020), university environment (Suhlmann et al., 2018; Nicoletti and do Carmo., 2019) and absence of university services to solve problems in the course process (Coppari et al., 2019).

From an educational approach, according to the results of empirical studies, the inability to cope with the performance-related demands of the higher education institution, false expectations, and lower identification with the course, as well as problems in financing the studies, are considered the most important reasons for dropout (Heublein, 2014). Likewise, in online university education, dropout is due to previous academic performance, age, gender, personal circumstances, and self-learning and online learning skills (Orellana et al., 2020). In addition, the lack of technological tools, such as online support tools for students, both academic and psychological, and everything related to the digital environment, generates helplessness in students, becoming a key dropout factor (Saldarriaga et al., 2021).

On the other hand, other factors relevant to a student’s tendency to drop out are the historical weighted average of their grades, the weighted average of their grades in the last cycle, or the number of credits passed in their courses (Vega et al., 2022). The critical problem for many tertiary-level students is learning how to learn. Learning can and should be learned, and the way to learn it is through guided-conscious learning (Kaschek, 2020).

In addition, the results show that the cost and burden on students, financial resources, qualitative and quantitative teacher characteristics, and university type/size significantly affect college dropout (Kim and Kim, 2018). From a social approach, other results indicate that graduates from more disadvantaged socioeconomic backgrounds experience difficulties completing their studies on time despite controlling for academic performance, educational behaviors, program characteristics, and institutional characteristics (Zarifa et al., 2018). Family situation and peer relationships correspond with students’ dropout intentions; three main findings are revealed: (1) parents’ educational aspirations negatively influence dropout tendency, (2) in general, students living in a couple have shown lower dropout intentions. However, a new association favors the tendency to drop out, and (3) the presence of close friends decreases students’ dropout intentions (Baalmann et al., 2022).

Equally important is the fact that students face a variety of stressors, such as independent living, adapting to a new social environment, and academic pressure, making them particularly susceptible to mental health problems (Shu et al., 2022).

From a mental approach, epidemiological studies have indicated that one in three college students has experienced at least one mental disorder in the past twelve months, with anxiety and depression being the most frequent diagnoses. Depressive and anxiety symptoms burden students and society considerably because of their association with strained interpersonal relationships and poor functioning (Auerbach et al., 2018).

Common mental disorders, such as depression and anxiety, often appear in college students during the transition to early adulthood. Mental health problems can severely affect students’ functioning, interpersonal relationships, and academic performance (Karyotaki et al., 2022).

College students experience higher rates of depression than the general population. Depression in students is often associated with risky behaviors, such as suicidal thoughts and attempts, non-suicidal self-injury, and substance abuse (Halicka and Kiejna, 2018; Mars et al., 2019). Early identification and treatment can improve depressive symptoms, increase academic performance, and prevent school dropout (Wang et al., 2021). However, most college students with mental health problems do not receive psychological care despite the counseling services available at many universities (Shu et al., 2022). If professionals could become familiar with the unique problems characteristic of the developmental stage and environment in which college students find themselves, they could better serve them, using technology to increase adherence (Karyotaki et al., 2022). As a result, students who drop out of college can suffer economic and psychological consequences if they do not complete a study program, affecting future job skills.

The current global higher education scenario reveals a complex interplay of challenges and developments, in which student dropout emerges as a critical problem, aggravated by the COVID-19 pandemic and exacerbated by pre-existing inequalities (Anders et al., 2021; Farcnik et al., 2022). With approximately 235 million students enrolled in universities, there is a wide disparity in enrollment rates, representing only 40% of the college-age population, evidencing significant differences across regions and countries (Öztürk and Dayıoğlu, 2023). The pandemic has disproportionately affected the most vulnerable (Gaynor and Wilson, 2020; Yeomans Cabrera and Silva Fuentes, 2022), forcing a rapid transition to online education, a modality for which many institutions were not adequately prepared, leading to a deterioration in the quality of teaching and learning (Barbour et al., 2020). This scenario reflects an urgent need for reform and adaptation in higher education to address both dropout rates and intrinsic inequalities in access and quality of education globally (Vidal et al., 2022). Therefore, a better understanding of dropout factors in higher education can contribute to the well-being of individual students and is essential to reducing college dropout rates and improving job skills for future generations (Bardach et al., 2020).

The hypotheses of this study are:

The present study was a cross-sectional descriptive study with a sample of 655 Chilean higher education students, with a maximum error of 3.8% for a 95% confidence interval, using the STROBE cross-sectional reporting guidelines (Von Elm et al., 2007). This study aimed to determine the psychosocial variables influencing the risk of dropping out of the higher education system in a sample of post-pandemic Chilean university students.

The inclusion criteria for participants in the study included being of legal age, studying in a higher education institution when answering the survey, having internet access, answering in the survey language (Spanish), and approving informed consent before beginning to answer the questionnaires. The exclusion criteria were incomplete questionnaires. There were two cases where participants answered the whole questionnaire but did not approve the informed consent. Those cases were not included in the data analysis.

This study aimed to determine the psychosocial variables influencing the risk of dropping out of the higher education system in a sample of post-pandemic Chilean university students. The present study sample consisted of 655 students from the Chilean educational system. The ages ranged from 18 to 62 years with a mean of 22.12 years (SD = 3.25), 77.4% of the sample was female, 22.6% was male, 575 (87.8%) belonged to Chilean universities, and 80 (12.2%) belonged to Chilean institutes or technical training centers. The sample’s characteristics and the description of the sociodemographic variables can be seen in Table 1.

The statistical association of the variables and the risk of dropping out was possible by performing a comparative statistical analysis through X2 between the students at risk of dropping out and those who were not. The details can be seen in Table 2.

To determine odds ratios relative to variables associated with dropout in higher education students, significantly associated variables in the categorical analysis were subjected to category analysis using 2 × 2 tables. Details can be found in Table 3.

From the logistic regression performed with the significant variables, we can see that the predictor variables of dropout risk in higher education students were failing four or more courses [AOR = 3.434; 95% CI: 1.272, 9.269], presenting depressive symptoms [AOR = 1.857; 95% CI: 1.214, 2.839], having suicidal ideation and thoughts [AOR = 2. 169; 95% CI: 1.509, 3. 118], having clinical insomnia [AOR = 2. 024; 95% CI: 1. 400, 2. 927], poor parental support [AOR = 1. 459; 95% CI: 1. 029, 2. 069], impaired performance [AOR = 1. 857; 95% CI: 1. 214, 2. 839], presenting suicidal ideation and thoughts [AOR = 2. 069], impaired performance during the pandemic [AOR = 1.882; 95% CI: 1.317, 2.690], and the impaired socioeconomic status during the pandemic [AOR = 1.649; 95% CI: 1.153, 2.357]. Details can be found in Table 4.

This study aimed to determine the psychosocial variables influencing the risk of dropping out of the higher education system in a sample of post-pandemic Chilean university students. The present investigation reported the main risk factors in Chilean higher education students. The discussion is divided into three macro sections corresponding to the study’s hypotheses.

Regarding our first hypothesis (students with high mental health problems are likelier to drop out), we confirmed that students with high mental health problems, such as depression, suicidal ideation, and clinical insomnia, tended to present a higher risk of dropping out.

Regarding our second hypothesis (students with low levels of personal and family support are likelier to drop out), we confirmed that students who perceived low parental support tended to present a higher risk of dropping out.

Regarding our third hypothesis (students with higher involvement rates in the COVID-19 pandemic are likelier to drop out), we confirmed that students with academic and socioeconomic problems during the COVID-19 pandemic presented higher risks of post-pandemic dropout.

Finally, regarding our main objective, we can state that the psychosocial variables influencing the risk of dropping out of the higher education system in a sample of post-pandemic Chilean university students are failing four or more courses, having depressive symptoms, having suicidal ideation thoughts, having clinical insomnia, low parental support, impaired performance during the pandemic and impaired socioeconomic status during the pandemic.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number (s) can be found at: https://drive.google.com/drive/folders/1AbHPvEYNEq5oX5Q_dvWpQ7cf5VAupCFM?usp=sharing.

The studies involving humans were approved by Central Bioethics Committee of the Universidad Andrés Bello, Registration No 024/2022. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual (s) for the publication of any potentially identifiable images or data included in this article.

JM-L: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Writing – original draft. M-MY-C: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing.