Risk and Protective Factors of Depressive Symptoms Among Hungarian Adolescents from a Large Cross-Sectional Survey

Abstract

There is a global documented rise in depressive symptoms among adolescents. The cross-sectional assessments in representative samples of adolescents would help explore their correlates, which may serve as resources for intervention. Our cross-sectional survey entitled “Békés County Youth Study 2024” involved a representative sample of grade-9 high school students (N = 1590, aged 15–17 years, 56.4% females) from public high schools in the region, Hungary. Besides depressive symptoms (measured by a validated, shortened version of the Children’s Depression Inventory, CDI), a set of psychosocial variables (psychosomatic symptoms, internet addiction, future orientation, social support from family and friends, marks, gender, school satisfaction, and religiosity) was included in the survey. Gender differences in the summary score for CDI were significant: t(1588) = −12.062, p < 0.001, showing a higher rate of females. A total of 22.6% (males: 13% and females: 30%) belonged to the group at risk of depression. All potential predictors proved significant, with the strongest contribution of psychosomatic symptoms (Beta = 0.306, p < 0.001), and the most relevant protective role of family support (Beta = −0.265, p < 0.001). Other than further exploring contextual factors that increase risk for and protect against adolescent depression, mental health promotion in schools should include well-being training.

1. Introduction

There is a global documented rise in depressive symptoms among adolescents, with increasing rates since around 2010–2012, consistent with increases in clinical depression and suicide [1]. Population variations highlight the importance of current determinants of risk, help provide evidence of emerging risk factors for understanding trends in adolescent mental health, and underscore the need for a comprehensive action plan [2]. Particularly, these aims should focus on preventable risk factors and protective factors to strengthen resilience [3]. An urgent need for increased action on prevention and early intervention should obtain priority in places with a shortage of mental health care, like Hungary, exacerbated by a declining number of mental health professionals, and inadequate resources and infrastructure, leading to longer waiting times and a strained workforce [4,5]. Exploring the mental health of the population living in a Hungarian disadvantaged region requires special attention in order to neutralize social, economic, and psychological disadvantages [6].

In a research context, depression can be interpreted in two ways. First, depression is a clinical disorder, diagnosed not only on the basis of a cut-off point of psychometric scales but also detailed patient examination, while depressive symptomatology (measured as a continuous variable) is an indicator of mental health which may or may not indicate a depressive disorder [7]. When mapping their contributing factors, it is useful to differentiate between the two terms in order to identify modes of intervention or prevention.

Among the risk factors of depressive symptoms, most epidemiological studies report gender differences, emerging during adolescence and continuing throughout adulthood: women, on average, have two times higher likelihood to develop depression than men [8]. This difference is influenced by a combination of biological (e.g., hormonal), psychological (e.g., stress reaction, emotion regulation), and social (e.g., bullying victimization, gender norms) factors; moreover, gender may also interact with other risk factors (e.g., unhealthy behaviors, chronic disease) [9,10]. These effects are more pronounced after the age of 14 years as compared to children aged 8–14 years [11]. A rising tendency among adolescents can be justified not only in the prevalence of depression but also in psychosomatic symptoms. The psychosomatic symptom score is a relevant indicator of youth psychosocial health, with a usual surplus among girls [12]. While most adolescents are free of serious somatic illness, there is an elevated focus on bodily experiences due to biological changes occurring during puberty. Several subjective health complaints, such as tiredness, nervousness, backache, headache, and sleeping problems, are the most psychosomatic symptoms reported [13]. These symptoms may have a great influence on their well-being and health perceptions [14]. Psychosomatic symptoms, with psychological origins, are common in individuals experiencing depression, and sometimes they can mask the underlying depression, or vice versa [15,16]. Among adolescents, the frequencies and persistence of psychosomatic complaints during adolescence were found to predict depression later in young adulthood [17]. As a risk factor, we should also mention adolescents’ digital life, in particular, internet addiction. For the relationship between depression and internet addiction, studies suggest a bidirectional relationship: internet addiction can contribute to depression through an elevated self-disclosure and negative online experiences, but depressed individuals may be more susceptible to developing internet addiction, searching for connection with others as a “therapy” for their loneliness, especially on social media [18,19,20]. This connection is particularly relevant in adolescents and young adults [21].

Among the factors that may provide protection, social support functions as a crucial protective factor against mental health adversities. High levels of social support can not only protect individuals from developing mental disorders but also help them in stressful situations through strengthening self-esteem and resilience, and coping with difficult life situations [22,23]. During the developmental transition of adolescence, children’s social network undergoes drastic changes with a growing effect of peer relations, and a changing role of the family [24]. However, this change does not deemphasize family members’ role in socialization; on the contrary, the context of family continues to provide guidance for youth in their decisions and attitude formation, although less so than before due to adolescents’ increased autonomy. Thus, parents can significantly contribute to their children’s positive development even during the adolescent period [25]. On the other hand, peer group effects can be either positive or negative on youth’s mental health, particularly today’s digital culture, where social media remarkably transforms adolescent peer relationships [26,27]. From the viewpoint of positive psychology, future orientation, that is, having goals and plans for the future instead of prioritizing immediate rewards, may also serve as a protection [28,29]. Similarly, spirituality/religiosity might also positively contribute to youth well-being, enhancing life satisfaction, providing a sense of meaning and purpose, fostering positive social relationships, and offering effective coping strategies [30,31]. In particular, spiritual well-being was found to be protective against depression, where one can find harmony and peace through the subjective experiences stemming from spiritual/religious feelings, and not necessarily from practicing religious guidelines [31]. Finally, school-related protective factors are also important in adolescent mental well-being. It refers to the students’ overall subjective appraisal of their school experience, encompassing their feelings and attitudes towards the school environment, its programs, impacting their academic motivation and achievement, as well as their well-being and mental health [32]. A negative correlation was found between adolescents’ depressive symptoms and their academic achievement in the UK; this relationship is rather bidirectional: depression can hinder successful school engagement, but low academic achievement can also contribute to depressive symptoms [33].

In this study, we aim to explore adolescents’ depressive symptoms in light of a set of psychosocial variables that may act as potential risk and protective factors. Beyond gender, we analyzed the role of social support from family and friends, religious and school-related factors, future orientation, internet addiction, and psychosomatic symptoms. We detected both bivariate relationships between the depressive symptom score and these variables, as well as applied multivariate analyses to find the most relevant predictors of depressive symptoms. We also calculated the likelihood of the risk of depression, using a recommended cut-off point. We anticipate that future orientation, social support variables, and school-related and religious factors might be positive correlates of adolescent life satisfaction, that is, protective factors. Other than gender, the psychosomatic symptom and internet addiction scores might be negative correlates, that is, risk factors.

2. Materials and Methods

2.1. Participants and Procedure

Participants of this survey were high school students in the 9th grade (age range between 15 and 17 years, Mean = 16.03 years, SD = 0.84; 56.4% females and 43.6% males) as part of the Békés County Youth Study 2024, organized by the Local Collaborative Forum for Drug Issues. The aim of this survey was to obtain actual information on the students’ health-related issues at this age group in Békés County (Hungary), in order to develop health education programs for youth. Altogether, 1600 students were randomly selected from nine districts of the county, with 1590 responses received. In order to substantiate the study’s representativeness, we applied stratification during the sampling procedure based on the county’s district, school type, and sex. The school types were as follows: 47.4% secondary modern school (with a focus on both general and technical education), 17.7% secondary vocational schools (with a focus on technical education), and 34.9% grammar school (with a focus on general education). Data were collected in May 2024 through an online survey. Google Drive forms were applied in data collection with the help of local organizers. The questionnaire took approximately 20–25 min to complete. The Institutional Review Board (IRB) of the Doctoral School of Education, University of Szeged, Hungary (no. 12/2024) approved the procedure. Participation was voluntary and anonymous. Before data collection, parental informed consent was obtained for their children’s participation.

2.2. Measures

2.2.1. Depressive Symptoms

For measuring depressive symptoms, we applied a validated, shortened version [34] of the original 27-item Children’s Depression Inventory (CDI) [35]. Each item assesses a single symptom, such as hopelessness or loss of interest, and they are coded from 1 to 3. A higher score indicated more depressive symptoms. In order to measure the risk of depression, we recoded the variables (from 0 to 2) and weighted the shortened CDI by a factor of 3.375 (number of original CDI items 27/shortened version items 8 = 3.375). Then, we applied the suggested cut-off point = 20 to define risk of (or potential) depression [34,35,36]. The scale was reliable with a Cronbach’s alpha of 0.82 with this sample.

2.2.2. Psychosomatic Symptoms

The psychosomatic symptom index measured the frequency of some common self-reported psychosomatic symptoms: tension headache, backache, chronic fatigue, sleeping problems, palpitation, stomach pyrosis, and tension diarrhea [37]. This index provided information on the frequency of these symptoms during the previous 12 months. Respondents were asked, ‘During the past 12 months, how often have you had backache?’, etc. Responses were coded as never (0), seldom (1), sometimes (2), and often (3). The summed score was coded from 0 to 21, with higher scores reflecting a greater frequency of experiencing these symptoms.

2.2.3. Future Orientation

Students’ attitudes towards the future were measured by an adapted and shortened Hungarian version [38] of The Consideration of Future Consequences Scale [39], consisting of six statements (e.g., “I only act to satisfy immediate concerns, figuring the future will take care of itself”, reverse item). The respondents should indicate the extent to which each statement, ranging from 1 (=extremely uncharacteristic) to 5 (=extremely characteristic). The final scores were coded from 6 to 30; a higher score indicates a high degree of future orientation. Cronbach’s alpha coefficient with this sample was 0.74.

2.2.4. Social Support

Two subscales of the Multidimensional Scale of Perceived Social Support (MSPSS) were applied to measure social support from family and friends [40]. We applied the Hungarian validated version [41]. Both subscales contained 4 items, e.g., “I get the emotional help and support I need from my family” (family support) or “I can count on my friends when things go wrong” (friend support). Adolescents indicate how strongly they agree with each statement on a 5-point Likert-type scale from not at all (1) to very much (5). Higher scores referred to greater levels of perceived social support. Both subscales were reliable with a Cronbach alpha (α) = 0.95.

2.2.5. Internet Addiction

Internet addiction was measured by the validated short (6-item) form of the Problematic Internet Use Questionnaire (PIUQ-SF-6) [42]. It was originally an 18-item scale, developed on a Hungarian sample, with good psychometric properties, validated on different samples in various countries. The scale assesses three dimensions of problematic internet use (i.e., neglect, obsession, control disorder). The respondents indicated the level of agreement with each statement on a 5-point Likert scale from never (1) to always/almost always (5). The final scores range from 6 to 30; higher scores indicate an increased level of addiction. The scale was reliable with a Cronbach alpha (α) = 0.77 with this sample.

2.2.6. School-Related and Religious Factors

Two school-related variables were included: school achievement and how the adolescents feel about their school, derived from previous national representative surveys [43]. Both variables are measured on a 7-point rating scale. School achievement was measured by the following question: “What grades do you mostly get in school?” Responses vary from 1 (mostly D’s and F’s, which is equivalent to 1 and 2 in the Hungarian school system) to 7 (mostly A’s, that is, 5). School satisfaction was measured with the question: “How happy are you with school right now?” The responses varied from 1 (not at all) to 7 (very much). The importance of religion in one’s life was measured by a question dealing with how much religion was important for the respondents from 1 (not at all) to 7 (very much) [43].

2.3. Data Analysis

SPSS for MS Windows Release 22.0 was used to analyze the data, with a maximum significance level set at 0.05. The analysis started with an examination of the descriptive statistics for the variables, and Student’s t-tests were used to detect gender differences. Bivariate relationships between variables were examined using Pearson correlation coefficients separately for boys and girls. Multiple linear regression analysis justified the risk and protective factors of the depressive symptom score. Finally, multivariate logistic regression analysis examined predictors of the risk of depression. An odds ratio (OR) > 1.0 indicates a positive association between the independent variables and depression (rate above the cut-off score), while OR < 1.0 indicates the opposite.

3. Results

3.1. Descriptive Statistics

Table 1. Descriptive statistics for study variables and gender differences (N = 1590).

	Minimum	Maximum	Total M (SD)	Boys (n = 694) M (SD)	Girls (n = 986) M (SD)	t-Test p-Value
Depressive symptoms (CDI)	8	24	11.19 (3.04)	10.19 (2.73)	11.97 (3.04)	−12.062 P < 0.001
Psychosomatic symptoms	0	21	8.42 (5.13)	6.20 (4.51)	10.13 (4.92)	−16.380 p < 0.001
Internet addiction	6	30	12.57 (4.42)	11.72 (4.12)	13.22 (4.53)	−6.780 p < 0.001
Future orientation	6	30	20.40 (3.80)	20.73 (3.14)	20.14 (3.69)	3.073 p = 0.002
Family support	4	28	20.79 (7.38)	21.66 (7.18)	20.11 (7.47)	4.169 p < 0.001
Friend support	4	28	22.15 (6.76)	21.46 (6.96)	22.68 (6.57)	−3.580 p < 0.001
School satisfaction	1	7	4.29 (1.47)	4.46 (1.51)	4.15 (1.42)	4.129 p < 0.001
School achievement	1	7	4.61 (1.48)	4.48 (1.50)	4.716 (1.47)	−3.137 p = 0.002
Importance of religion	1	7	3.00 (1.89)	3.23 (1.98)	2.80 (1.81)	4.438 p < 0.001

Notes: Student t-test.

provides descriptive statistics for the study variables and gender differences with the results of t-tests.

Analyses revealed that, compared to boys, girls reported significantly higher levels of depressive [t(1590) = −12.062, p < 0.001] and psychosomatic [t(1590) = −16.380, p < 0.001] symptoms. Girls also scored higher on the internet addiction scale [t(1590) = −6.780, p < 0.001]. Social support from friends was higher among girls, but social support from family was the reverse. Although school satisfaction showed greater mean scores for boys [t(1590) = 4.129, p < 0.001], the level of school achievement was higher among girls [t(1590) = −3.127, p = 0.002]. For boys, religiosity seemed slightly more important.

3.2. Bivariate Associations

Table 2. Zero-order correlation matrix for study variables by sex (N = 1590).

Variable (Scores)	1	2	3	4	5	6	7	8	9
1. Depressive symptoms (CDI)	-	0.386 ***	0.334 ***	−0.263 ***	−0.447 ***	−0.320 ***	−0.377 ***	−0.163 ***	−0.70
2. Psychosomatic symptoms	0.502 ***	-	0.307 ***	0.004	−0.162 ***	−0.027	−0.219 ***	−0.122 **	0.027
3. Internet addiction	0.325 ***	0.344 ***	-	−0.103 **	−0.074 *	−0.082 *	−0.104 **	−0.076 *	−0.030
4. Future orientation	−0.226 ***	0.024	−0.085 *	-	0.362 ***	0.265 ***	0.168 ***	0.255 ***	0.124 **
5. Family support	−0.483 ***	−0.230 ***	−0.154 ***	0.263 ***	-	0.534 ***	0.239 ***	0.148 ***	0.072
6. Friend support	−0.264 ***	−0.024	−0.044	0.269 ***	0.352 ***	-	0.220 ***	0.148 ***	0.053
7. School Satisfaction	−0.387 ***	−0.283 ***	−0.137 ***	0.177 ***	0.245 ***	0.224 ***	-	0.251 ***	0.032
8. School achievement	−0.287 ***	−0.115 **	−0.149 ***	0.263 ***	0.272 ***	0.207 ***	0.229 ***	-	0.061
9. Importance of religion	−0.115 **	−0.041	−0.062	0.038	0.098 **	−0.028	0.127 ***	−0.007	-

Notes. r = correlation coefficients: * p < 0.05, ** p < 0.01, *** p < 0.001. Boys’ results (n = 694) above the diagonal, girls’ results (n = 896) below.

shows the zero-order correlation matrix for study variables, separately for boys and girls.

Both among girls and boys, the depression score was positively correlated with psychosomatic symptoms and internet addiction; the association was particularly high between girls’ depressive and psychosomatic symptoms (r = 0.502; p < 0.001). Among the protective factors, both types of social support showed a negative association, especially family support (r = −0.447; p < 0.001 for boys and r = −0.483; p < 0.001 for girls). Other protective factors were also significantly and negatively related to the CDI score, except for the importance of religion among boys.

3.3. Multivariate Linear Regression Analysis

Results for multiple linear regression analysis are seen in

Table 3. Multiple linear regression estimates for depressive symptoms (CDI scores) (N = 1590).

	Total		Boys		Girls
Independent Variables	B/SE/β	p-value	B/SE/β	p-Value	B/SE/β	p-Value
Risk factors    Psychosomatic symptoms    Internet addiction	0.18/0.01/0.31 0.10/0.01/0.15	p < 0.001 p < 0.001	0.14/0.02/0.23 0.14/0.02/0.20	p < 0.001 p < 0.001	0.21/0.02/0.34 0.08/0.02/0.12	p < 0.001 p < 0.001
Protective factors    Future orientation    Social support from family    Social support from friends    School satisfaction    School achievement    Importance of religion	−0.05/0.02/−0.07 −0.11/0.01/−0.26 −0.04/0.01/−0.08 −0.35/0.04/−0.17 −0.08/0.04/−0.04 −0.06/0.03/−0.04	p = 0.001 p < 0.001 p < 0.001 p < 0.001 p = 0.049 p = 0.044	−0.06/0.02/−0.09 −0.10/0.01/−0.27 −0.03/0.01/−0.08 −0.38/0.06/−0.21 0.04/0.06/0.02 −0.04/0.04/−0.03	p = 0.007 p < 0.001 p = 0.016 p < 0.001 p = 0.492 p = 0.315	−0.05/0.02/−0.06 −0.11/0.01/−0.27 −0.04/0.01/−0.09 −0.32/0.06/−0.15 −0.18/0.05−0.09 −0.08/0.04−0.05	p = 0.022 p < 0.001 p = 0.002 p < 0.001 p = 0.001 p = 0.055
Gender	0.63/0.12/0.10	p < 0.001	-		-
Constant R2 F value	13.589 * 0.485 165.430		13.582 * 0.408 58.956		15.353 * 0.468 94.426

Notes. B = unstandardized regression coefficient, SE = standard error, β = standardized regression coefficients. VIF (variance inflation factor) < 2.0 in each case. * p < 0.001.

.

All the included risk and protective factors proved significant predictors when analyzing the whole sample. The psychosomatic symptom score was the strongest predictor (β = 0.31, p < 0.00), while internet addiction was also a positive contributor (β = 0.15, p < 0.001). Among the protective factors, we should highlight the role of family support (β = 0.26, p < 0.001). School achievement and the importance of religion were less relevant. Instead of school achievement, satisfaction with school provided more protection. Gender differences were found only in the role of school achievement, which was a more relevant protective factor for girls.

In

Table 4. Frequencies of depression risk by sex (N = 1590).

CDI Score	Range	N (%)	Boys N (%)	Girls N (%)
Normal	<19	1231 (77.4)	604 (87.0)	627 (70.0)
At risk of depression	≥20	359 (22.6)	90 (13.0)	269 (30.0)
Chi-square			65.068 (p < 0.001)

Notes. Rates are 2.31 times higher among females than among males.

, we can see percentages of the depression risk: 22.6% of the students may be at risk of depression, 13.0% of male adolescents, and 30% of females. Namely, girls are 2.31 times more at risk than boys of having depression.

Finally,

Table 5. Multivariate logistic regression estimates (OR) of the psychosocial variables on the depression risk (N = 1590).

	Total			Boys			Girls
Correlates	B (SE)	OR (95% CI)	p	B (SE)	OR (95% CI)	p	B (SE)	OR (95% CI)	p
Psychosomatic symptoms	0.16 (0.02)	1.18 (1.14; 1.22)	p < 0.001	0.14 (0.03)	1.15 (1.09; 1.22)	p < 0.001	0.18 (0.02)	1.19 (1.14; 1.25)	p < 0.001
Internet addiction	0.08 (0.02)	1.09 (1.05; 1.12)	p < 0.001	0.12 (0.03)	1.12 (1.06; 1.19)	p < 0.001	0.06 (0.02)	1.06 (1.02; 1.11)	p = 0.002
Future orientation	−0.04 (0.02)	0.96 (0.92; 100)	p = 0.062	−0.06 (0.04)	0.94 (0.87; 1.02)	p = 0.123	−0.03 (0.03)	0.97 (0.92; 1.02)	p = 0.221
Social support from family	−0.08 (0.01)	0.92 (0.90; 0.94)	p < 0.001	−0.08 (0.02)	0.92 (0.88; 0.96)	p < 0.001	−0.08 (0.01)	0.92 (0.90; 0.94)	p < 0.001
Social support from friends	−0.05 (0.01)	0.95 (0.93; 0.98)	p < 0.001	−0.05 (0.02)	0.95 (0.91; 0.99)	p = 0.009	−0.04 (0.01)	0.96 (0.93; 0.98)	p = 0.002
School satisfaction	−0.25 (0.05)	0.78 (0.70; 0.86)	p < 0.001	−0.32 (0.09)	0.73 (0.61; 0.87)	p = 0.001	−0.21 (0.07)	0.81 (0.71; 0.93)	p = 0.003
School achievement	−0.12 (0.05)	0.88 (0.80; 0.98)	p = 0.018	−0.03 (0.09)	0.97 (0.81; 1.16)	p = 0.714	−0.180 (0.06)	0.83 (0.73; 0.95)	p = 0.005
Importance of religion	−0.05 (0.04)	0.95 (0.87; 1.03)	p = 0.178	−0.01 (0.07)	0.98 (0.86; 1.13)	p = 0.832	−0.08 (0.05)	0.92 (0.83; 1.02)	p = 0.117
Gender (girl = 2)	0.55 (0.17)	1.74 (1.25; 2.43)	p = 0.001	-			-
χ2 df Nagelkerke R2	531.128 * 9 0.433			158.643 * 8 0.380			310.777 * 8 0.4150

Notes. B = unstandardized regression coefficient, SE = standard error, OR = odds ratio, 95% CI = 95% confidence intervals. * p < 0.001.

shows the results for multivariate logistic regression analyses to detect the most relevant predictor of the depression risk. Future orientation and the importance of religion were not significant predictors. School achievement decreased the likelihood of developing depression only in females. Among the risk factors, both the psychosomatic symptom scores and internet addiction elevate the risk of depression, while the social support variables (especially family support) and school satisfaction served as protective factors in both genders.

4. Discussion

Preventing adolescent depression involves fostering resilience and psychological well-being, with special attention to vulnerable youth. The risk and protection research concept provides an appropriate framework for detecting risk and protective factors and promoting early intervention [44]. Epidemiological studies help provide useful information on the frequencies of depressive symptoms and depression, as well as their contributors, even though these data are indicative only and cannot provide the whole picture for a clinical diagnosis. Clinical investigations identified the CDI summary score of 20 as the optimal screening cut-off score with high sensitivity and specificity [34,35,36]. In our study, the prevalence of potentially depressed adolescents was 22.6%, with a 2.31 times higher rate among girls than boys. This gender ratio was similar to that of adult populations [8]. The number of depressive symptoms usually tends to increase between the ages of 12 and 14 years, and gender differences emerge at age 14 [11]. Concerning gender, the finding of a 2.3-fold increased risk in girls requires several aspects of an explanatory framework, including biological and psychological factors [9,10,11], also incorporating socio-cultural factors such as gender norms, academic expectations, and the burden of emotional labor/psychological distress carried by young women [45,46].

Among the risk factors of both depressive symptoms and depression, the frequency of psychosomatic symptoms seems to have a decisive role. Psychosomatic symptoms are an important indicator of stress, psychological well-being, and mental health. In our study, the psychosomatic symptom score was much higher among girls, indicating greater vulnerability to the impact of stress as the stress reactivity model suggests [11]. Gender differences in the response to stress are partly influenced by the increased sex hormone secretion in puberty, beyond the learned coping strategies [47]. In bivariate analyses, the psychosomatic symptom score showed the strongest correlation with the depressive symptoms score in both genders, particularly among girls. Likewise, the other risk factors, namely, internet addiction was a significant correlate of adolescent depressive symptoms, not only in bivariate but also in multivariate analyses. Previous studies suggest a bidirectional association with depression [18,19,20]. While being connected to the internet, especially to social media, is particularly relevant for adolescents [21], more attention should be given to address the issue of potential threats to mental health in the virtual world. Recently, this can be most readily observed across multiple platforms where interpersonal dialog has been largely and “pathologically” supplanted by ‘iPhone artifice’. Depressed youth often feel lonely, and social media provides a platform of connections for them with temporary and illusory relief [19]. The internet addiction score was also positively correlated with psychosomatic symptoms, indicating that compulsive internet use is not a suitable resource for enhancing psychological well-being.

Among the protective factors, we should pinpoint the role of social support from family. In both genders, family support was negatively correlated with depressive and psychosomatic symptoms, internet addiction, future orientation, and with school satisfaction and academic achievement, and, among girls, also with religiosity. In multivariate analyses, family support proved the strongest predictor of depressive symptomatology and provided the strongest protection against the risk of depression. These findings support the theory of the family’s continuing impact on children’s psychological well-being and mental health [24,25]. Although the role of parents seems to be decreasing, they still act as essential resources for providing a secure environment, emotional support, guidance, and the development of a value system for youth, maybe less evidently but vigorously. Social support from friends seemed to provide less protection, but also contributed to a lower risk of depression. In addition, supports from family and friends were also related. Our results are in line with previous findings that having supportive relationships is a key protective factor against adolescent mental health problems; in addition, the different sources of social support can reduce the effect of adversity experienced elsewhere, e.g., in school [48]. Our findings, however, refer to a potential cumulative effect: social support from family and friends, and school satisfaction are significantly related. School satisfaction seems to be a stronger protective factor than school achievement: those with a greater level of satisfaction with school life reported not only lower levels of depressive and psychosomatic symptoms, as well as internet addiction, but also higher levels of future orientation. In addition, in multivariate analyses, school satisfaction remained a significant predictor of the CDI score, unlike school achievement, future orientation, and religiosity. Today, young people’s temporal orientations are heavily shaped by collective and global challenges, such as climate change, modifying their perceptions of the present and the future [49]. Similarly, religion may lose its protective role in modern society due to a decreasing rate in youth’s religiosity [50]. Accordingly, while social support from family remained a strong protective factor, other resources of protection might change. In terms of religiosity and future orientation, their lack of statistical significance may be interpreted in light of secularization processes and the generational uncertainty in the face of global challenges. It would also be interesting to deepen this angle by linking these findings to the socio-cultural dynamics of youth resilience, moving beyond strictly individualistic frameworks.

In terms of the strength of our study, besides the relatively large sample size, we should mention the representativeness of the sample and the high response rate. In addition, we applied validated and culturally adapted measurements in the survey. Including both potential risk and protective factors is useful for further understanding adolescent mental health. However, we should also mention some limitations as well. First, cause-and-effect relationships cannot be justified due to the cross-sectional nature of data collection. As mentioned earlier, the data on self-reported depressive symptoms do not allow us define depression; we can only estimate the risk of depression. Although the decision to employ a shortened version of the Children’s Depression Inventory (CDI) demonstrates an attempt to balance practicality and validity, the weighting strategy used to align it with the original version requires stronger psychometric justification. Future research should include further risk and protective factors, e.g., actual level of stress or other resilience factors, or coping strategies. Alternative analytical models would also be recommended to further explore these associations, e.g., multilevel models. In addition, there is a need for longitudinal and qualitative studies that capture young people’s lived experiences of suffering and their relationship with social supports. Finally, intervention studies and measuring the effectiveness of prevention programs would be useful to demonstrate how they may lower the frequency of depressive and psychosomatic symptoms.

5. Conclusions

The long-term cumulative effect of exposure to childhood adversities is well documented, but as it seems, multiple protective factors may act together to promote adolescent mental health and foster resilience and adaptation [48]. While the cumulative effect of exposure to both risks and protections may be validated, the compensation model suggests a conscious intervention to overwrite negative experiences with positive ones [51]. Overall, a potential cumulative effect between social support, family, friends, and school satisfaction is significantly associated with positive mood affect. It is also notable that while several traditional protective factors play a lesser role in adolescent mental health, such as future orientation or religiosity, others, like social support from family, continue to provide strong protection. Our findings suggest that introducing stress management programs for youth should include learning effective stress-reducing techniques, such as progressive muscle relaxation, or cognitive-behavioral techniques for identifying stressors and recognizing adaptive coping strategies. These interventions can strengthen psychological well-being, improve both depressive and psychosomatic symptoms, and help prevent the development of depression [52]. Other than stress management, social support would also be essential to prevent psychological and mental health problems, involving actively nurturing existing relationships and fostering meaningful connections, instead of online activities on social media. The acquisition of social and communication skills is strongly associated with positive youth development; thus, intervention should incorporate these skills [53]. Finally, our findings also suggest that school satisfaction may provide strong protection against adolescent poor mental health; therefore, enhancing school engagement and school connectedness should be a priority in schools [54]. Therefore, besides mental health promotion programs, structural and institutional interventions, including school-based well-being policies, reduction in territorial mental-health inequalities, and community-supported programs for families, would also be recommended. As a final remark, we should also underscore the biosocial nature of adolescent depression, which is shaped by bodies (e.g., hormones), emotions, families, schools, and social structures. A complex viewpoint may help clarify the valid nature of adolescent depression; therefore, it is essential to situate these findings within a broader framework of social determinants and place greater emphasis on social and territorial inequalities as fundamental determinants of youth psychological well-being [55]. This approach may advocate a multiscale prevention approach that integrates interventions at the micro (individual skills), meso (family and school), and macro (public policy and inequality reduction) levels.