Next Article in Journal
Factors Affecting the Developmental Status of Children Aged 6 Months to 2 Years in Urban and Rural Areas
Next Article in Special Issue
Comorbidities Affecting Children with Autism Spectrum Disorder: A Retrospective Chart Review
Previous Article in Journal
Pain Evaluation and Treatment in Children: A Practical Approach
Previous Article in Special Issue
Physiological Responses during Parental Conflicts as Potential Biomarkers for Adolescent Depression
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Children
Volume 10
Issue 7
10.3390/children10071213
children-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Effects of Social Determinants and Resilience on the Mental Health of Chilean Adolescents

by
Alejandra Caqueo-Urízar
1,*,
Patricio Mena-Chamorro
2,
Diego Henríquez
2,
Alfonso Urzúa
3 and
Matías Irarrázaval
4,5
1
Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
2
Centro de Justicia Educacional CJE, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
3
Escuela de Psicología, Universidad Católica del Norte, Antofagasta 1270709, Chile
4
Department of Psychiatry, Faculty of Medicine, Clinical Hospital, Universidad de Chile, Santiago 8380453, Chile
5
Millennium Institute for Research in Depression and Personality, MIDAP, Santiago 8380453, Chile
*
Author to whom correspondence should be addressed.
Children 2023, 10(7), 1213; https://doi.org/10.3390/children10071213
Submission received: 20 June 2023 / Revised: 4 July 2023 / Accepted: 11 July 2023 / Published: 13 July 2023
(This article belongs to the Special Issue Child and Adolescent Psychiatry and Mental Health Progress)
Review Reports Versions Notes

Abstract

:
The aim of this research was to evaluate the effects of social determinants (i.e., gender, educational vulnerability, and socioeconomic status) and resilience on the mental health of Chilean adolescents in pre-, during, and post-COVID-19 pandemic contexts. The study included a group of 684 students, ranging in age from 12 to 18 years, who were attending educational institutions in the city of Arica. The Child and Adolescent Assessment System (SENA) was used to measure mental health problems, the Brief Resilience Scale for Children and Youth (CYRM-12) was used to measure resilience, and the Vulnerability Index of Educational Institutions was used to measure educational vulnerability. The results suggest increases in depressive, anxious, and social anxiety symptomatologies over time (wave by year, 2018, 2020, and 2021). In addition, multiple linear regression models showed predictive effects of the COVID-19 pandemic, gender, vulnerability index, socioeconomic status, and resilient behaviors on mental health problems. The worsening of mental health indicators over time requires the greater coordination and integration of mental health experts in the most vulnerable educational centers.

1. Introduction

In recent years, the mental health of millions of people has systematically worsened [1]. In 2019, 970 million people reported living with a mental disorder, with anxiety disorders and depression being more prevalent [2]. In 2020, the number of people living with a mental disorder increased significantly due to the COVID-19 pandemic [1]. Although effective prevention and treatment options exist, access to effective care is limited for most people with mental disorders [3].
The social context in which human beings develop plays a fundamental role in the well-being and care for people’s mental health [4]. The high frequency of occurrence and severity of mental disorders are strongly correlated with individuals’ socio-economic circumstances, including factors such as poverty, income inequality, or involuntary migration [5,6]. The social determinants of mental health that stand out the most in the literature are demographic, which include gender, age, ethnicity, and life expectancy [7,8,9,10,11]; economic, where financial and employment status, housing, and income inequality are considered [12,13,14,15]; an individual’s area of residence, which is related to variables such as safety, the availability of services, and places of recreation [16,17,18,19]; environmental events, such as natural disasters and armed conflicts, as well as hazards to the ecosystem due to climate change [20,21,22]; and the sociocultural domain, which encompasses education, interpersonal relationships, social capital, culture, and a person’s sense of belonging in their community [23,24,25].
Given the influence of social determinants on mental health problems, it has been shown that for a group of subjects, exposure to the same traumatic and/or adverse event will produce different reactions and/or responses [26]. One of these responses is the resilient response which, in other words, implies a positive adaptation to adversity [27,28] that reduces anxious and depressive symptoms [29,30,31,32]. People’s resilient responses are influenced by individual and family factors [33,34]. Also, the presence of resilient behaviors has been demonstrated in adolescents who have been exposed to various adversities and traumas [35]. Despite the relevance of resilience as a protective factor for mental health problems [36], few studies have focused on studying the effects of resilience in conjunction with social determinants of mental health in Latin American adolescents [37,38]. The literature suggests that most mental health problems begin in the first years of life [39,40], during adolescence, which is a phase characterized by significant vulnerability, when negative social encounters have the potential to detrimentally impact the cognitive, emotional, and behavioral growth of individuals [39]. Likewise, the infant–juvenile population is also affected by social determinants. Female adolescents have higher risks of developing depressive and anxiety disorders [41,42], while males are associated with a greater likelihood of developing a substance use disorder [8,43,44]. Likewise, children and adolescents from low socioeconomic backgrounds encounter significant disparities in education, social opportunities, and healthcare [45], making them two to three times more susceptible to developing mental health issues compared to their counterparts from higher socioeconomic strata [46]. In addition, adolescents who are exposed to high levels of community violence, the deprivation of services and a scarcity of recreational areas in residential neighborhoods in this population have shown higher prevalences of psychotic and depressive disorders, as well as greater likelihoods of substance abuse and bullying [16,17,47]. In addition, it has been observed that family relationships also have an important impact on the development of mental disorders in children and adolescents, as in the case of anxious and depressive disorders [48,49], while friendship relationships and social support can decrease these possibilities, establishing themselves as protective factors [50].
On the other hand, COVID-19 has had a significant impact on mental health worldwide [31]. The pandemic and measures of confinement and social distancing have created a stressful and challenging environment that has exacerbated existing mental health problems and resulted in new disorders in many individuals [27]. Depression and anxiety are two of the most common disorders affected by the pandemic due to uncertainty about health, fear of infection or contagion, loss of employment, and social isolation [36,37]. In addition, social anxiety has also been affected due to social distancing and the use of masks, which has hindered social interactions and increased feelings of loneliness and isolation [30,37]. Despite these challenges, the resilience of many people has been remarkable as they find ways to cope and maintain their mental health [26,27,28].
In Chile, the available findings point in the same direction as those presented in other international contexts [44,51,52]. For example, studies show that the most recurrent health problems in the infant–juvenile population in northern Chile are anxiety, depression, and conduct disorders, and the female population turns out to be the most affected compared to males [44,51]. Economic inequality has proven to be a very relevant factor influencing the mental health of adolescents since the educational level, access to health care, and psychosocial problems faced by individuals this population will depend on the socioeconomic status of their family. Chile’s economic and social approach promotes the privatization of essential services, which hinders equal access to these services. Government policies during the COVID-19 pandemic maintained extensive and demanding confinement measures in both labor and school environments. This situation had a negative impact on the upbringing of the most disadvantaged children, as well as on their parents’ abilities to meet their children’s emotional, educational, and health needs. As a result, these children became more vulnerable when facing challenges relating to their mental health [44,53,54,55].
Despite the existence of some studies contrasting the effects of the social determinants of adolescent mental health in the Chilean context, the research is still insufficient and/or scarce. Moreover, no study has jointly contrasted the effects of the COVID-19 pandemic, resilience, and social determinants on the mental health of young people. Hence, the objective of this research was to assess the impact of social determinants and resilience on the mental well-being of Chilean adolescents within the context of the COVID-19 pandemic, before, during, and after its occurrence.

2. Materials and Methods

2.1. Participants

A repeated cross-sectional design [56] was used with three samples of high school students belonging to urban educational establishments in 2018, 2021, and 2022. Data collection was performed via a non-probability convenience sampling strategy [57]. The 2018 sample was composed of 249 students, of which 52.2% (n = 130) attended educational establishments with low vulnerability indexes and 47.8% (n = 119) attended educational establishments with high vulnerability indexes. The ages of the students ranged from 12 to 18 years, with a mean age of 14.42 (SD = 1.83); 50.2% (n = 125) reported being female, 90% (n = 224) reported being Chilean, 70.3% (n = 175) reported belonging to a religion, and 67.1% (n = 167) reported no ethnicity (the term no ethnicity refers to the absence of a specific ethnic or racial identity. This option is for individuals who do not identify with any particular ethnic group or prefer not to disclose their ethnicity). The 2021 sample was composed of 206 students, with 47.6% (n = 98) attending low-vulnerability-index educational institutions and 52.4% (n = 108) attending high-vulnerability-index educational institutions. The ages of the students ranged from 12 to 18 years, with a mean age of 14.23 (SD = 1.59); 55.3% (n = 114) reported being female, 87.4% (n = 180) reported being Chilean, 55.3% (n = 114) reported belonging to a religion, and 58.7% (n = 121) reported having no ethnicity. The 2022 sample was composed of 229 students in which 40.2% (n = 94) attend low-vulnerability-index educational institutions, and 59.8% (n = 140) attend high-vulnerability-index educational institutions. The ages of the students ranged from 12 to 18 years, with a mean age of 14.48 (SD = 1.55), 56.3% (n = 129) reported being female, 88.0% (n = 206) reported being Chilean, 56.5% (n = 130) reported belonging to a religion, and 57.1% (n = 129) reported having no ethnicity. The sociodemographic details are presented in Table 1.

2.2. Instruments

Ad-hoc sociodemographic questionnaire: This questionnaire collected information on the students’ sex, age, socioeconomic status, nationality, religiosity, and ethnicity. The year of data collection was used as a proxy for the effect of the COVID-19 pandemic.
Vulnerability index of educational institutions: This index was calculated by estimating the weighted percentage of the risk needs of students attending different educational institutions; some of the factors encompassed in this category are limited maternal education, unaddressed medical necessities, existential needs, insufficient weight for age, and other related aspects. According to the statistics presented in the Annual Municipal Development Plan of Arica [58], a classification of “high” vulnerability and “low” vulnerability was made. According to this report, between 2014 and 2017, public schools exhibited an average vulnerability index of 86%, surpassing the 77% vulnerability level observed in the commune [59]. Conversely, during the same timeframe, subsidized and private schools had an average vulnerability index of 74%, which remained lower than the percentage observed at the communal level [59]. The cut-off point was the average vulnerability percentage of the commune. The most elevated score in the low vulnerability category was 73, while the lowest score in the high vulnerability category was 78.
Child and adolescent assessment system (Sistema de Evaluación de Niños y Adolescentes, SENA): This instrument measures several emotional and behavioral problems [60]. Self-report versions were used for high school students 12 to 18 years old. The high school version was composed of 188 items; however, for this study, only the items pertaining to the internalized problem dimensions (e.g., depression, anxiety, and social anxiety, 32 items in total) were used. The items were on a five-point Likert behavioral/attitudinal statement scale (from 1 = “never or almost never” to 5 = “always or almost always”). Higher scores suggest the presence of a higher level of maladaptation and difficulties in adapting to the context. The version used in this study reported evidence of reliability and validity based on the internal structure of the test (Sánchez-Sánchez et al., 2016). The SENA scales have presented Cronbach’s alpha coefficients greater than 0.75 [61].
Brief Resilience Scale for Children and Youth (CYRM-12) [62]: This scale measures the degree of resilience in the face of adversity based on the interaction between individual, relational, community, and cultural factors (e.g., “I try to finish what I start” and “My family will be there for me in difficult times”). Response options respond to behavioral statements on a 5-point Likert scale (from 1 = “never” to 5 = “very much”). Higher scores suggest higher levels of resilience. Llistosella et al. [63] translated and validated the 32-item version of the Child and Youth Resilience Measure (CYRM) into Spanish and reported evidence of validity of their scores, reporting a satisfactory internal structure of the test and satisfactory reliability coefficients (α > 0.80).

2.3. Procedures

The study, which is part of a larger project from the Educational Justice Center, was approved by the Ethics Committee of the Universidad de Tarapacá (26–2017) on 20 September 2017. The researchers reached out and extended voluntary invitations to principals and counselors from educational institutions in Arica to partake in this study. Out of the 42 schools invited, 29 principals agreed to participate in the data collection for the years 2018, 2021, and 2022, resulting in a refusal rate of 31%.
Parental consent was sought after explaining the purpose and scope of the study. The students then signed a form agreeing to participate. The questionnaires were administered in a pencil and paper format by at least 2 trained assistants. The application procedure took place within each class for every course, in which a minimum of two trained interviewers, along with the respective teachers of the courses, responded to the questionnaire. The duration was approximately 45 min, and data collection sessions were conducted from March to December of 2018, 2021, and 2022.

2.4. Data Analysis

Initially, to characterize the sample in each year, the proportion of the students’ sociodemographic variables was obtained and the central tendency (i.e., mean), dispersion (i.e., standard deviation, minimum, and maximum), and shape (i.e., skewness and kurtosis) of the continuous variables were obtained. The Shapiro–Wilk test was also used to assess univariate normality.
Afterward, a statistical technique called an analysis of variance (ANOVA) was utilized to assess and compare the variations in the average scores of the depression, anxiety, and social anxiety scales across different years of application (serving as a proxy to gauge the impact of the COVID-19 pandemic). Multiple comparisons were conducted with a Games–Howell correction because the data did not show homoscedasticity (depression: Levene’s test, F = 4.64, p = 0.010; anxiety: Levene’s test, F = 5.24, p = 0.006; and social anxiety: Levene’s test, F = 9.52, p < 0.001), and the partial eta squared (η2p) was used as an estimate of effect size. Parametric analyses were used because ANOVAs are sufficiently robust with data that did no present homoscedasticity and are not normally distributed either (see Table 2) [64].
Finally, to evaluate the potential predictive capacities of social determinants and resilience regarding students’ mental health indexes, multiple linear regression analyses were performed. One model was estimated for depression, another for anxiety, and a final model for social anxiety. Gender, the year of application (i.e., used as a proxy to measure the effect of the COVID-19 pandemic), vulnerability index, socioeconomic status (SES), nationality, ethnicity, being Aymara (The Aymara are an indigenous ethnic group with a strong presence in the Andean region that includes the territories of Chile, Peru, and Bolivia), religion, and resilience were included as predictor variables. The categorical predictor variables were transformed into dummy variables. The standardized ß coefficients represented changes in the standard deviations of the criterion variables. Predictor variables with higher standardized ß coefficients suggest a greater relative effect on student mental health indexes. All assumptions were met. The presence of multicollinearity among predictor variables was ruled out via the inflated variance factor (IVF), for which the values were less than 5 for all variables. The residuals were independent of each other (depression: Durbin–Watson statistic, DW = 2.02, p = 0.880, anxiety: DW = 2.03, p = 0.782, social anxiety: DW = 2.03, p = 0.722). The presence of homoscedasticity was verified by examining a scatter plot of the predictors and standardized residuals. The normality of the residuals for each dependent variable was assessed via the examination of a histogram and a Q-Q plot of the standardized residuals. The statistical hypothesis testing for the data analyses was conducted at a significance level of 5%. The statistical analyses were carried out using Rstudio and Jamovi version 2.3.2 software.

3. Results

3.1. Descriptive Analysis

Symmetry and kurtosis were outside the acceptable ranges to be considered normally distributed in the 2018 and 2021 collections, with the exception of 2022. In addition, the Shapiro–Wilk test showed that none of the mental health variables had a normal distribution in the 2018, 2021, and 2022 collections. Details of the descriptive analyses of the study variables are presented in Table 2.
The ANOVA showed statistically significant differences between the different years of application and the mental health indexes. Regarding the dependent variable of depression, the main effect of application year was statistically significant (F (2, 443) = 19.24, p < 0.001). Post-hoc test results showed a significant increase in depression scores between the application years 2018 and 2021 (M = 2.11, SD = 0.86, M = 2.47, SD = 1.01, t = −4.02, p < 0.001, respectively), as well as 2018 and 2022 (M = 2.59, SD = 0.92, t = −5.97, p < 0.001). There were no statistically significant differences between 2021 and 2022 in depression scores (t = −1.36, p = 0.362). According to the dependent variable of anxiety, the main effect of tge year of application was also statistically significant (F (2, 441) = 25.21, p < 0.001). Post-hoc test results showed a significant increase in anxiety scores between the application years 2018 and 2021 (M = 2.48, SD = 0.84, M = 2.92, SD = 1.00, t = −4.94, p < 0.001, respectively), as well as 2018 and 2022 (M = 3.04, SD = 0.96, t = −6.64, p < 0.001). There were no statistically significant differences between 2021 and 2022 in depression scores (t = −1.23, p = 0.434). Finally, regarding the dependent variable of social anxiety, the main effect of the year of application was also statistically significant (F (2, 438) = 20.91, p < 0.001). Post-hoc test results showed a significant increase in social anxiety scores between the application years 2018 and 2021 (M = 2.38, SD = 0.77, M = 2.79, SD = 0.98, t = −4.86, p < 0.001, respectively), as well as 2018 and 2022 (M = 2.82, SD = 0.89, t = −5.78, p < 0.001). There were no statistically significant differences between 2021 and 2022 in depression scores (t = −0.34, p = 0.935).

3.2. Multiple Linear Regression Models on Mental Health Indexes

The results of the multiple linear regression analyses showed that for the depression criterion variable, the model was statistically significant (F (11) = 23.8, p < 0.001) and able to explain 27.3% of the variance in the students’ depression scores. There is a significant and negative relationship between depression and resilience scores when all other variables remain constant (i.e., their value was zero). Thus, with an increase in one standard deviation of resilience, the depression score will decrease on average by −0.706. Depression scores increased on average by 0.247 and 0.592 when the application years were 2021 and 2022, compared to 2018 when, all other variables remained constant. The depression scores decreased on average by −0.424 when students were male compared to female when all other variables remained constant. The depression scores increased on average by 0.204 in students belonging to families with medium SESs compared to a low SES when all other variables were held constant. The details of the multiple linear regression are presented in Table 3.
For the anxiety criterion variable, the multiple linear regression model was statistically significant (F (11) = 15.54, p < 0.001) and able to explain 19.3% of the variance in the students’ anxiety scores. There is a significant and negative relationship between anxiety and resilience scores when all other variables are held constant. Thus, with an increase in one standard deviation of resilience, the anxiety score will decrease on average by −0.473. Anxiety scores increased on average by 0.385 and 0.780 when the application years were 2021 and 2022 compared to 2018 with all other variables held constant. Anxiety scores decreased on average by −0.548 when the students were male compared to female, with all other variables remaining constant. Anxiety scores decreased on average by −0.183 in students belonging to families with low EVIs compared to a high EVI when all other variables remained constant. The details of the multiple linear regression are presented in Table 3.
For the social anxiety criterion variable, the multiple linear regression model was statistically significant (F (11) = 12.76, p < 0.001) and able to explain 16.2% of the variance of students’ social anxiety scores. There is a significant and negative relationship between social anxiety and resilience scores when all other variables are held constant. Thus, with an increase in one standard deviation of resilience, the social anxiety score will decrease on average by −0.386. Social anxiety scores increased on average by 0.302 and 0.644 when the application years were 2021 and 2022, compared to 2018, with all other variables held constant. Social anxiety scores decreased on average by −0.534 when students were male compared to female, with all other variables remaining constant. Social anxiety scores increased on average by 0.181 in students belonging to families with medium SESs compared to a low SES when all other variables were held constant. The details of the multiple linear regression are presented in Table 3.

4. Discussion

The purpose of the present study was to evaluate the effects of social determinants and resilience on the mental health of Chilean adolescents in pre-, during-, and post-COVID-19 pandemic contexts. This study showed that students attending schools with high and low vulnerability indexes experienced significantly worsened mental health over the years from 2018 (i.e., pre-pandemic) to 2021 (i.e., during the pandemic) and 2022 (i.e., post-pandemic). This finding is consistent with the ample evidence of the negative effects of the COVID-19 pandemic on adolescent mental health [65,66,67,68,69,70,71,72,73]. Therefore, it is necessary to design interventions that provide tools to facilitate the return to the classroom, especially in context of vulnerability.
This study also showed increases in depressive, anxious, and social anxiety symptomatologies when the students were female, their family had a medium SES (i.e., only in depression and social anxiety), and they belonged to educational establishments with high vulnerability indexes (i.e., only in anxiety), while the resilient response of the students was able to reduce mental health problems. These findings are also consistent with those found in the literature [29,30,31,32,37,38], with the exception of the socioeconomic status factor, which may seem contradictory. One might expect worse mental health in families with low socioeconomic levels since it is usually assumed that families with low economic resources possess greater vulnerability [74]. However, it is important to note that in this study, the variable SES was constructed in terms of the schools attended by the students (i.e., public, subsidized, and private schools). Therefore, there is a possibility that the participation of families with low and medium SESs between public and subsidized schools is not very clear. It is possible that the majority of families with low SESs are participating in subsidized schools and vice versa. In this sense, the vulnerability index could complement this contradictory finding. Similar to how numerous public schools were categorized as having a low vulnerability index, certain subsidized schools were identified as having a high vulnerability index. As a result, these outcomes partially align with previous findings documented in the existing literature [12,13,14,15,23,24,25,43,53,54,55].
Some factors that may support these results probably respond to the difficult access that students belonging to families from low and middle socioeconomic strata have to treatment, the negative effects of the COVID-19 pandemic, and the perceptions of uncertainty, loneliness, and/or of not possessing the necessary tools to perform adequately in the neoliberal system that prevails in Chile [43,53,75,76]. The existence of disparities in anxiety levels, according to the vulnerability index in school communities, could indicate the importance of enriching standardized and generalist interventions. This would imply adapting these interventions to the specific context of vulnerability faced by adolescents.
This study has some limitations. First, a non-probabilistic sampling strategy was used, which limits the generalizability of the results to other sociocultural contexts. Secondly, only students from one region of northern Chile participated; therefore, future research should contrast these results with students from other regions of the country. Third, although a repeated cross-sectional design was used which allows for the establishment of pseudo-longitudinal explanations [77], it is not possible to establish changes over time in the students (i.e., trajectories); therefore, future studies should use longitudinal designs that allow the findings of this study to be contrasted with those in a sample of adolescents measured at different times. Finally, this study did not consider other sources of information that contribute to a better understanding of adolescent mental health, such as information reports from parents, guardians, and/or teachers.
Despite the limitations, the findings of this study provide relevant information about the resilient responses of high school students in Chile attending low- and high-vulnerability educational establishments pre-, during, and post-COVID-19 pandemic and the pandemic’s effects on their mental health. The results suggest the design of interventions with new perspectives based on the students’ context of vulnerability. It also provides background information for future research to include variables not considered in this study and to test more complex explanatory models. Likewise, the findings of this study can be used as inputs to propose new orientations in the design of public policies that produce structural changes in the reduction of the mental health needs of Chilean high school students. Finally, it is suggested to strengthen the coordination between mental health experts and the most vulnerable educational centers, implement programs to promote resilience, address socioeconomic and educational inequalities, promote a comprehensive health approach in the educational curriculum, and establish psychological and emotional support programs within schools. These suggestions aim to address social determinants and promote mental health in the educational context to improve student well-being.

Author Contributions

Conceptualization, A.C.-U., P.M.-C., D.H. and A.U.; methodology, A.C.-U., P.M.-C., D.H. and A.U.; formal analysis, A.C.-U., P.M.-C., D.H. and M.I.; investigation, A.C.-U., P.M.-C., D.H. and A.U.; writing—original draft preparation, A.C.-U., P.M.-C., D.H. and M.I.; writing—review and editing, A.C.-U., P.M.-C., D.H. and M.I.; supervision, A.C.-U. and A.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Agencia Nacional de Investigación y Desarrollo de Chile (ANID), grant ANID PIA CIE 160007.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Universidad de Tarapacá (26-2017, 20 September 2017).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare that they have no conflict of interest. We hereby declare that all figures and tables included in the manuscript are our original creation. We have not used third-party copyrighted material.

References

  1. World Health Organization. Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact; WHO: Geneva, Switzerland, 2022.
  2. Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx). 2023. Available online: https://vizhub.healthdata.org/gbd-results/ (accessed on 14 May 2023).
  3. Charlson, F.; van Ommeren, M.; Flaxman, A.; Cornett, J.; Whiteford, H.; Saxena, S. New WHO prevalence estimates of mental disorders in conflict settings: A systematic review and meta-analysis. Lancet 2019, 394, 240–248. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Dinesh, B.; Tribe, R.; Poulter, D. Social justice, health equity, and mental health. S. Afr. J. Psychol. 2022, 52, 3–10. [Google Scholar]
  5. Erik, B.; Kurup, A.S. (Eds.) Equity, Social Determinants and Public Health Programmes; World Health Organization: Geneva, Switzerland, 2010.
  6. World Health Organization. Calouste gulbenkian foundation. In Social Determinants of Mental Health; World Health Organization: Geneva, Switzerland, 2014; Volume 191. [Google Scholar]
  7. Kemp, K.; Griffiths, J.; Campbell, S.; Lovell, K. An exploration of the follow-up up needs of patients with inflammatory bowel disease. J. Crohn’s Colitis 2013, 7, e386–e395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Cheng, H.G.; Deng, F.; Xiong, W.; Phillips, M.R. Prevalence of alcohol use disorders in mainland China: A systematic review. Addiction 2015, 110, 761–774. [Google Scholar] [CrossRef] [PubMed]
  9. Musliner, K.L.; Munk-Olsen, T.; Eaton, W.W.; Zandi, P.P. Heterogeneity in long-term trajectories of depressive symptoms: Patterns, predictors and outcomes. J. Affect. Disord. 2016, 192, 199–211. [Google Scholar] [CrossRef] [Green Version]
  10. Shah, A. Psychiatry of old age and ethnic minority older people in the United Kingdom. Rev. Clin. Gerontol. 2009, 19, 119–134. [Google Scholar] [CrossRef]
  11. Qian, J.; Hu, Q.; Wan, Y.; Li, T.; Wu, M.; Ren, Z.; Yu, D. Prevalence of eating disorders in the general population: A systematic review. Shanghai Arch. Psychiatry 2013, 25, 212–223. [Google Scholar]
  12. Iemmi, V.; Bantjes, J.; Coast, E.; Channer, K.; Leone, T.; McDaid, D.; Palfreyman, A.; Stephens, B.; Lund, C. Suicide and poverty in low-income and middle-income countries: A systematic review. Lancet Psychiatry 2016, 3, 774–783. [Google Scholar] [CrossRef] [Green Version]
  13. Kulik, D.M.; Gaetz, S.; Crowe, C.; Ford-Jones, E. Homeless youth’s overwhelming health burden: A review of the literature. Paediatr. Child Health 2011, 16, e43–e47. [Google Scholar] [CrossRef] [Green Version]
  14. Lund, C.; Breen, A.; Flisher, A.J.; Kakuma, R.; Corrigall, J.; Joska, J.A.; Swartz, L.; Patel, V. Poverty and common mental disorders in low and middle income countries: A systematic review. Soc. Sci. Med. 2010, 71, 517–528. [Google Scholar] [CrossRef] [Green Version]
  15. Vameghi, M.; Rafiey, H.; Sajjadi, H.; Rashidian, A. Disadvantages of being a street child in Iran: A systematic review. Int. J. Adolesc. Youth 2014, 19, 521–535. [Google Scholar] [CrossRef]
  16. Julien, D.; Richard, L.; Gauvin, L.; Kestens, Y. Neighborhood characteristics and depressive mood among older adults: An integrative review. Int. Psychogeriatr. 2012, 24, 1207–1225. [Google Scholar] [CrossRef] [Green Version]
  17. O’donoghue, B.; Roche, E.; Lane, A. Neighbourhood level social deprivation and the risk of psychotic disorders: A systematic review. Soc. Psychiatry 2016, 51, 941–950. [Google Scholar] [CrossRef]
  18. Richardson, R.; Westley, T.; Gariépy, G.; Austin, N.; Nandi, A. Neighborhood socioeconomic conditions and depression: A systematic review and meta-analysis. Soc. Psychiatry 2015, 50, 1641–1656. [Google Scholar] [CrossRef]
  19. Wu, Y.-T.; Prina, A.M.; Brayne, C. The association between community environment and cognitive function: A systematic review. Soc. Psychiatry 2015, 50, 351–362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Dai, W.; Chen, L.; Lai, Z.; Li, Y.; Wang, J.; Liu, A. The incidence of post-traumatic stress disorder among survivors after earthquakes:a systematic review and meta-analysis. BMC Psychiatry 2016, 16, 188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Goldmann, E.; Galea, S. Mental Health Consequences of Disasters. Annu. Rev. Public Health 2014, 35, 169–183. [Google Scholar] [CrossRef] [PubMed]
  22. Kirmayer, L.J.; Narasiah, L.; Munoz, M.; Rashid, M.; Ryder, A.G.; Guzder, J.; Hassan, G.; Rousseau, C.; Pottie, K. For the Canadian Collaboration for Immigrant and Refugee Health (CCIRH) Common mental health problems in immigrants and refugees: General approach in primary care. Can. Med. Assoc. J. 2010, 183, E959–E967. [Google Scholar] [CrossRef] [Green Version]
  23. Araújo, B.Y.; Borrell, L.N. Understanding the Link between Discrimination, Mental Health Outcomes, and Life Chances among Latinos. Hisp. J. Behav. Sci. 2006, 28, 245–266. [Google Scholar] [CrossRef]
  24. Beydoun, M.A.; Beydoun, H.A.; Gamaldo, A.A.; Teel, A.; Zonderman, A.B.; Wang, Y. Epidemiologic studies of modifiable factors associated with cognition and dementia: Systematic review and meta-analysis. BMC Public Health 2014, 14, 643. [Google Scholar] [CrossRef] [Green Version]
  25. Klomek, A.B.; Sourander, A.; Elonheimo, H. Bullying by peers in childhood and effects on psychopathology, suicidality, and criminality in adulthood. Lancet Psychiatry 2015, 2, 930–941. [Google Scholar] [CrossRef] [PubMed]
  26. Coulombe, S.; Pacheco, T.; Cox, E.; Khalil, C.; Doucerain, M.M.; Auger, E.; Meunier, S. Risk and Resilience Factors During the COVID-19 Pandemic: A Snapshot of the Experiences of Canadian Workers Early on in the Crisis. Front. Psychol. 2020, 11, 580702. [Google Scholar] [CrossRef] [PubMed]
  27. Vannini, P.; Gagliardi, G.P.; Kuppe, M.; Dossett, M.L.; Donovan, N.J.; Gatchel, J.R.; Quiroz, Y.T.; Premnath, P.Y.; Amariglio, R.; Sperling, R.A.; et al. Stress, resilience, and coping strategies in a sample of community-dwelling older adults during COVID-19. J. Psychiatr. Res. 2021, 138, 176–185. [Google Scholar] [CrossRef] [PubMed]
  28. Park, C.L.; Finkelstein-Fox, L.; Russell, B.S.; Fendrich, M.; Hutchison, M.; Becker, J. Psychological resilience early in the COVID-19 pandemic: Stressors, resources, and coping strategies in a national sample of Americans. Am. Psychol. 2021, 76, 715–728. [Google Scholar] [CrossRef]
  29. Magson, N.R.; Freeman, J.Y.A.; Rapee, R.M.; Richardson, C.E.; Oar, E.L.; Fardouly, J. Risk and Protective Factors for Prospective Changes in Adolescent Mental Health during the COVID-19 Pandemic. J. Youth Adolesc. 2021, 50, 44–57. [Google Scholar] [CrossRef] [PubMed]
  30. Sampogna, G.; Del Vecchio, V.; Giallonardo, V.; Luciano, M.; Albert, U.; Carmassi, C.; Carrà, G.; Cirulli, F.; Dell’osso, B.; Menculini, G.; et al. What Is the Role of Resilience and Coping Strategies on the Mental Health of the General Population during the COVID-19 Pandemic? Results from the Italian Multicentric COMET Study. Brain Sci. 2021, 11, 1231. [Google Scholar]
  31. Bravo-Sanzana, M.; Miranda, R.; Oriol, X. Adolescent Victimization during COVID-19 Lockdowns and Its Influence on Mental Health Problems in Seven Countries: The Mediation Effect of Resilience. Int. J. Environ. Res. Public Health 2023, 20, 1958. [Google Scholar] [CrossRef]
  32. Verdolini, N.; Amoretti, S.; Montejo, L.; García-Rizo, C.; Hogg, B.; Mezquida, G.; Rabelo-Da-Ponte, F.D.; Vallespir, C.; Radua, J.; Martinez-Aran, A.; et al. Resilience and mental health during the COVID-19 pandemic. J. Affect. Disord. 2021, 283, 156–164. [Google Scholar] [CrossRef]
  33. Rosenberg, A.R. Cultivating Deliberate Resilience during the Coronavirus Disease 2019 Pandemic. JAMA Pediatr. 2020, 174, 817–818. [Google Scholar] [CrossRef] [Green Version]
  34. Masten, A.S. Ordinary magic. Resilience processes in development. Am. Psychol. 2001, 56, 227–238. [Google Scholar]
  35. Feldman, R. What is resilience: An affiliative neuroscience approach. World Psychiatry 2020, 19, 132–150. [Google Scholar]
  36. Petzold, M.B.; Bendau, A.; Plag, J.; Pyrkosch, L.; Mascarell Maricic, L.; Betzler, F.; Rogoll, J.; Große, J.; Ströhle, A. Risk, resilience, psychological distress, and anxiety at the beginning of the COVID-19 pandemic in Germany. Brain Behav. 2020, 10, e01745. [Google Scholar] [CrossRef] [PubMed]
  37. Zainel, A.A.; Qotba, H.; Al-Maadeed, A.; Al-Kohji, S.; Al Mujalli, H.; Ali, A.; Al Mannai, L.; Aladab, A.; AlSaadi, H.; AlKarbi, K.A.; et al. Psychological and Coping Strategies Related to Home Isolation and Social Distancing in Children and Adolescents During the COVID-19 Pandemic: Cross-sectional Study. JMIR Form. Res. 2021, 5, e24760. [Google Scholar]
  38. Duarte, C.S.; Monk, C.; Weissman, M.M.; Posner, J. Intergenerational psychiatry: A new look at a powerful perspective. World Psychiatry 2020, 19, 175–176. [Google Scholar] [PubMed]
  39. Lund, C.; Brooke-Sumner, C.; Baingana, F.; Baron, E.C.; Breuer, E.; Chandra, P.; Haushofer, J.; Herrman, H.; Jordans, M.; Kieling, C.; et al. Social determinants of mental disorders and the Sustainable Development Goals: A systematic review of reviews. Lancet Psychiatry 2018, 5, 357–369. [Google Scholar] [CrossRef] [PubMed]
  40. Polanczyk, G.V.; Salum, G.A.; Sugaya, L.S.; Caye, A.; Rohde, L.A. Annual Research Review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. J. Child Psychol. Psychiatry 2015, 56, 345–365. [Google Scholar] [CrossRef]
  41. Kouider, E.B.; Petermann, F. Common risk factors of depressive and anxiety symptomatics in childhood and adolescence: A systematic review from transdiagnostic perspectives. Fortschritte Neurol.-Psychiatr. 2015, 83, 321–333. [Google Scholar]
  42. Luppa, M.; Sikorski, C.; Luck, T.; Ehreke, L.; Konnopla, A.; Wiese, B.; Weyerer, S.; König, H.-H.; Riedel-Heller, S.G. Age- and Gender-Specific Prevalence of Depression in Latest-Life—Systematic Review and Meta-Analysis. J. Affect. Disord. 2012, 136, 212–221. [Google Scholar] [CrossRef]
  43. Caqueo-Urízar, A.; Flores, J.; Escobar, C.; Urzúa, A.; Irarrázaval, M. Psychiatric disorders in children and adolescents in a middle-income Latin American country. BMC Psychiatry 2020, 20, 104. [Google Scholar] [CrossRef] [Green Version]
  44. Caqueo-Urízar, A.; Mena-Chamorro, P.; Flores, J.; Narea, M.; Irarrázaval, M. Problemas de regulación emocional y salud mental en adolescentes del norte de Chile. Ter. Psicol. 2020, 38, 203–222. [Google Scholar] [CrossRef]
  45. Vukojevic, M.; Zovko, A.; Talić, I.; Tanović, M.; Resić, B.; Vrdoljak, I.; Splavski, B. Parental Socioeconomic Status as a Predictor of Physical and Mental Health Outcomes in Children—Literature Review. Acta Clin. Croat. 2017, 56, 742–748. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Reiss, F. Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Soc. Sci. Med. 2013, 90, 24–31. [Google Scholar] [CrossRef] [PubMed]
  47. Kelly, S. The Psychological Consequences to Adolescents of Exposure to Gang Violence in the Community: An Integrated Review of the Literature. J. Child Adolesc. Psychiatr. Nurs. 2010, 23, 61–73. [Google Scholar] [CrossRef] [PubMed]
  48. Burt, S.A. Rethinking environmental contributions to child and adolescent psychopathology: A meta-analysis of shared environmental influences. Psychol. Bull. 2009, 135, 608–637. [Google Scholar] [CrossRef]
  49. Cerdá, M.; Sagdeo, A.; Johnson, J.; Galea, S. Genetic and environmental influences on psychiatric comorbidity: A systematic review. J. Affect. Disord. 2010, 126, 14–38. [Google Scholar] [CrossRef] [Green Version]
  50. Bauer, A.; Stevens, M.; Purtscheller, D.; Knapp, M.; Fonagy, P.; Evans-Lacko, S.; Paul, J. Mobilising social support to improve mental health for children and adolescents: A systematic review using principles of realist synthesis. PLoS ONE 2021, 16, e0251750. [Google Scholar] [CrossRef]
  51. Caqueo-Urízar, A.; Urzúa, A.; Escobar-Soler, C.; Flores, J.; Mena-Chamorro, P.; Villalonga-Olives, E. Effects of Resilience and Acculturation Stress on Integration and Social Competence of Migrant Children and Adolescents in Northern Chile. Int. J. Environ. Res. Public Health 2021, 18, 2156. [Google Scholar] [CrossRef]
  52. Urzúa, A.; Caqueo-Urízar, A.; Aragón, D. Prevalencia de sintomatología ansiosa y depresiva en migrantes colombianos en Chile. Rev. Méd. Chile 2020, 148, 1271–1278. [Google Scholar] [CrossRef]
  53. Waissbluth, M.; Arredondo, C.; Quiroga, V.; Diez, S. Las restricciones a la reforma educativa en Chile: Perspectiva neoinstitucional y escenarios futuros. Rev. CLAD Reforma Democr. 2010, 47, 5–46. [Google Scholar]
  54. Villalobos, M.V.P.; Mujica, A.D.; Reichhardt, E.V. Características psicológicas de adolescentes pertenecientes a comunidades’ educativas vulnerables. Psicothema 2005, 17, 37–42. [Google Scholar]
  55. Flores, J.; Caqueo-Urízar, A.; Ramírez, C.; Díaz, P.; Durán, C.; López, L. Internalizing Problems and Resilience in Primary School Students in Low and High Socioeconomic Vulnerability Establishments in Chile. J. Sch. Health 2022, 92, 794–803. [Google Scholar] [CrossRef] [PubMed]
  56. Ato, M.; López-García, J.; Benavente, A. Un sistema de clasifi-cación de los diseños de investigación en psicología. Anales Psicol. 2013, 29, 1038–1059. [Google Scholar] [CrossRef] [Green Version]
  57. Otzen, T.; Manterola, C. Técnicas de Muestreo sobre una Población a Estudio. Int. J. Morphol. 2017, 35, 227–232. [Google Scholar] [CrossRef] [Green Version]
  58. Departamento de Administración de Educación Municipal (DAEM). Plan Anual de Desarrollo Educativo Municipal (PADEM). Chile: Municipalidad de Arica—Ley de Transparencia. 2018. Available online: https://transparencia.municipalidaddearica.cl/page.php?p=149 (accessed on 5 January 2023).
  59. Junta Nacional de Auxilio Escolar y Becas (JUNAEB). Prioridades 2017 Con IVE Sinae Basica Media Y Comunal. IVE—JUNAEB. 2017. Available online: https://www.junaeb.cl/ive (accessed on 5 January 2023).
  60. Fernández-Pinto, I.; Santamaría, P.; Sánchez-Sánchez, F.; Carrasco, M.A.; Del Barrio, V. (SENA), Sistema de Evaluación de Niños y Adolescentes, Manual técnico. TEA Ediciones. 2015. Available online: https://web.teaediciones.com/ejemplos/sena_manual_aplicacion_extracto.pdf (accessed on 15 March 2018).
  61. Sánchez-Sánchez, F.; Fernández-Pinto, I.; Santamaría, P.; Carrasco, M.; del Barrio, V. (SENA), Sistema de Evaluación de Niños y Adolescentes: Proceso de desarrollo y evidencias de fiabilidad y validez. Rev. Psicol. Clínica Con Niños Adolesc 2016, 3, 23–34. [Google Scholar]
  62. Liebenberg, L.; Ungar, M.; LeBlanc, J.C. The CYRM-12: A Brief Measure of Resilience. Can. J. Public Health 2013, 104, e131–e135. [Google Scholar] [CrossRef]
  63. Llistosella, M.; Gutiérrez-Rosado, T.; Rodríguez-Rey, R.; Liebenberg, L.; Bejarano, Á.; Gómez-Benito, J.; Limonero, J.T. Adaptation and Psychometric Properties of the Spanish Version of Child and Youth Resilience Measure (CYRM-32). Front. Psychol. 2019, 10, 1410. [Google Scholar] [CrossRef]
  64. Pablo, F.; Ocaña, J. Pretesting strategies for homoscedasticity when comparing means. Their robustness facing non-normality. Commun. Stat.-Simul. Comput. 2022, 51, 280–292. [Google Scholar]
  65. Aksunger, N.; Vernot, C.; Littman, R.; Voors, M.; Meriggi, N.F.; Abajobir, A.; Beber, B.; Dai, K.; Egger, D.; Islam, A.; et al. COVID-19 and mental health in 8 low- and middle-income countries: A prospective cohort study. PLoS Med. 2023, 20, e1004081. [Google Scholar] [CrossRef]
  66. Bouter, D.C.; Zarchev, M.; de Neve-Enthoven, N.G.M.; Ravensbergen, S.J.; Kamperman, A.M.; Hoogendijk, W.J.G.; Mil, N.H.G.-V. A longitudinal study of mental health in at-risk adolescents before and during the COVID-19 pandemic. Eur. Child Adolesc. Psychiatry 2023, 32, 1109–1117. [Google Scholar] [CrossRef]
  67. Witteveen, A.B.; Young, S.Y.; Cuijpers, P.; Ayuso-Mateos, J.L.; Barbui, C.; Bertolini, F.; Cabello, M.; Cadorin, C.; Downes, N.; Franzoi, D.; et al. COVID-19 and common mental health symptoms in the early phase of the pandemic: An umbrella review of the evidence. PLoS Med. 2023, 20, e1004206. [Google Scholar] [CrossRef]
  68. Wolf, K.; Schmitz, J. Scoping review: Longitudinal effects of the COVID-19 pandemic on child and adolescent mental health. Eur. Child Adolesc. Psychiatry 2023, 1–56. [Google Scholar] [CrossRef] [PubMed]
  69. Bernardini, F.; Attademo, L.; Rotter, M.; Compton, M.T. Social Determinants of Mental Health as Mediators and Moderators of the Mental Health Impacts of the COVID-19 Pandemic. Psychiatr. Serv. 2021, 72, 598–601. [Google Scholar] [CrossRef] [PubMed]
  70. Imran, N.; Zeshan, M.; Pervaiz, Z. Mental health considerations for children & adolescents in COVID-19 Pandemic. Pak. J. Med. Sci. 2020, 36, S67–S72. [Google Scholar]
  71. Jones, E.A.K.; Mitra, A.K.; Bhuiyan, A.R. Impact of COVID-19 on Mental Health in Adolescents: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 2470. [Google Scholar] [CrossRef]
  72. De Figueiredo, C.S.; Sandre, P.C.; Portugal, L.C.; Mázala-de-Oliveira, T.; da Silva Chagas, L.; Raony, Í.; Ferreira, E.S.; Giestal-de-Araujo, E.; Dos Santos, A.A.; Bomfim, P.O. COVID-19 pandemic impact on children and adolescents’ mental health: Biological, environmental, and social factors. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2021, 106, 110171. [Google Scholar] [CrossRef]
  73. de Miranda, D.M.; da Silva Athanasio, B.; Oliveira, A.C.S.; Simoes-e-Silva, A.C. How is COVID-19 pandemic impacting mental health of children and adolescents? Int. J. Disaster Risk Reduct. 2020, 51, 101845. [Google Scholar] [CrossRef]
  74. Mata, S.; Gómez-Pérez, M.M.; Calero, M.D. Resolución de problemas interpersonales en ninos en exclusion social: Valoracionde un programa de entrenamiento. Rev. Latinoam Psicol. 2018, 50, 103–113. [Google Scholar]
  75. Massazza, A.; Kienzler, H.; Al-Mitwalli, S.; Tamimi, N.; Giacaman, R. The association between uncertainty and mental health: A scoping review of the quantitative literature. J. Ment. Health 2022, 32, 480–491. [Google Scholar] [CrossRef] [PubMed]
  76. Cooper, K.; Hards, E.; Moltrecht, B.; Reynolds, S.; Shum, A.; McElroy, E.; Loades, M. Loneliness, social relationships, and mental health in adolescents during the COVID-19 pandemic. J. Affect. Disord. 2021, 289, 98–104. [Google Scholar] [CrossRef]
  77. Yee, J.L.; Niemeier, D. Advantages and disadvantages: Longitudinal vs. repeated cross-section surveys. Proj. Battelle 1996, 94, 7. [Google Scholar]
Table
Table 1. Sociodemographic characteristics of the samples.
Table 1. Sociodemographic characteristics of the samples.
2018 (n = 249)2021 (n = 206)2022 (n = 229)
M (SD) ± Range or n (%)M (SD) ± Range or n (%)M (SD) ± Range or n (%)
Sex
Female125 (50.2%)114 (55.3%)129 (56.3%)
Male124 (49.8%)92 (44.7%)100 (43.7%)
Age 14.42 (1.83) ± 12–1814.23 (1.59) ± 12–18 14.48 (1.55) ± 12–19
EVI
Low vulnerability130 (52.2%)98 (47.6%)94 (40.2%)
High vulnerability119 (47.8%)108 (52.4%)140 (59.8%)
SES
Low92 (36.9%)48 (23.3%)144 (61.5%)
Middle127 (51.0%)158 (76.7%)71 (30.3%)
High 30 (12.0%)0 (0.0%)19 (8.1%)
Nationality
Chilean224 (90%)180 (87.4%)206 (88.0%)
Foreigner25 (10%)26 (12.6%)28 (12.0%)
Religion
With religion175 (70.3%)114 (55.3%)130 (56.5%)
Without religion 74 (29.7%)90 (43.7)100 (43.5%)
Not reported0 (0%)2 (1%)0 (0%)
Ethnic group
With ethnicity82 (32.9%)85 (41.3%)97 (42.9%)
No ethnicity 167 (67.1%)121 (58.7%)129 (57.1%)
Aymara
Aymara58 (23.3%)68 (33.0%)75 (33.2%)
No-Aymara191 (76.7%)138 (67.0%)151 (66.8%)
M = mean; n = number of subjects; SD = standard deviation. EVI = Educational Vulnerability Index; SES = socioeconomic status.
Table
Table 2. Descriptive statistics of the dependent variables of the study.
Table 2. Descriptive statistics of the dependent variables of the study.
MSDMin–MaxS aK aShapiro–Wilkp
2018
Resilience3.870.562.1–5.0−2.90−0.210.9810.002
Depression2.110.861.0–4.86.471.130.908<0.001
Anxiety2.480.841.1–5.05.180.370.944<0.001
Social anxiety2.380.771.0–4.64.140.420.962<0.001
2021
Resilience3.730.661.8–5.0−3.27−0.120.971<0.001
Depression2.471.011.0–5.03.57−1.530.946<0.001
Anxiety2.921.001.0–5.01.53−2.140.973<0.001
Social anxiety2.790.981.0–5.02.11−1.740.970<0.001
2022
Resilience3.690.612.8–5.0−1.42−1.080.9890.064
Depression2.590.921.0–5.0 1.77−2.280.973<0.001
Anxiety3.040.961.0–5.0−0.13−2.950.977<0.001
Social anxiety2.820.831.0–4.91.55−2.740.969<0.001
a Standardized coefficient.
Table
Table 3. Results of the multiple regression analysis.
Table 3. Results of the multiple regression analysis.
VariablesDepressionAnxietySocial Anxiety
F(df) 23.8 (11)15.54 (11)12.76 (11)
p<0.001<0.001<0.001
Adj. R20.2730.1930.162
Standardized coefficients and value p
βpβpβp
Intercept4.699---4.307---3.92---
Resilience−0.706<0.001−0.473<0.001−0.386<0.001
20210.2470.0020.385<0.0010.302<0.001
20220.592<0.0010.780<0.0010.644<0.001
Men−0.424<0.001−0.0548<0.001−0.534<0.001
High EVI0.0240.762−0.1830.0330.0180.826
Middle SES0.2040.0160.1470.1040.1810.036
High SES0.2040.1640.1610.3090.0990.509
Migrant−0.0770.449−0.1310.2300.0650.529
No ethnicity−0.0620.5700.0450.705−0.1150.309
No Aymara0.1010.3920.0150.904−0.1130.352
Without religion−0.0540.422−0.0810.265−0.0130.846
Note: F = F statistic; p = Significance; adj. R2 = corrected R-squared coefficient; β = standardized regression coefficient; EVI = Educational Vulnerability Index; SES = Socioeconomic Status. Bold values indicate statistical significance (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Caqueo-Urízar, A.; Mena-Chamorro, P.; Henríquez, D.; Urzúa, A.; Irarrázaval, M. The Effects of Social Determinants and Resilience on the Mental Health of Chilean Adolescents. Children 2023, 10, 1213. https://doi.org/10.3390/children10071213

AMA Style

Caqueo-Urízar A, Mena-Chamorro P, Henríquez D, Urzúa A, Irarrázaval M. The Effects of Social Determinants and Resilience on the Mental Health of Chilean Adolescents. Children. 2023; 10(7):1213. https://doi.org/10.3390/children10071213

Chicago/Turabian Style

Caqueo-Urízar, Alejandra, Patricio Mena-Chamorro, Diego Henríquez, Alfonso Urzúa, and Matías Irarrázaval. 2023. "The Effects of Social Determinants and Resilience on the Mental Health of Chilean Adolescents" Children 10, no. 7: 1213. https://doi.org/10.3390/children10071213

APA Style

Caqueo-Urízar, A., Mena-Chamorro, P., Henríquez, D., Urzúa, A., & Irarrázaval, M. (2023). The Effects of Social Determinants and Resilience on the Mental Health of Chilean Adolescents. Children, 10(7), 1213. https://doi.org/10.3390/children10071213

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop