Skip to main content

Predictors of suicidal behaviors among school-going adolescents: a cross sectional study in Indonesia

Abstract

Background

Adolescents are a high-risk age group for committing suicide, and the risk substantially increases from early to late adolescence. Adolescence also serves as critical time period for early detection and intervention to prevent suicidal behaviors. This study aimed to assess the prevalence of suicidality and identify significant predictors of suicidality among adolescents.

Methods

A cross-sectional observational study was conducted between January-December 2023. Adolescents aged 14–18 years old (n = 2317) were consecutively recruited from 15 high schools across four provinces on Java Island in Indonesia. Self-reported validated instruments in Indonesian were used to assess sociodemographic profiles, self-esteem (RSES), hopelessness (BHS), loneliness (ULS-3), perceived social support (MSPSS), depression (PHQ-9), resilience (CD-RISC-10), suicidality (SBQ-R). With adjusted odds ratio (AOR) and 95% confidence interval (CI), binary logistic regression analysis was used to determine significant predictors of suicidality.

Results

The prevalence of lifetime suicide ideation was 26.5%, lifetime suicide plans were 18.2%, lifetime suicide threat was 14.1%, and lifetime suicide attempt was 4.4%. The prevalence of 12-month suicide ideation was 43.1%. The following variables were identified and significantly associated with suicidality (p < 0.05): female students (AOR = 1.912; 95%CI:1.507–2.425), chronic illness (AOR = 2.886; 95%CI:1.545–5.389), low resilience (AOR = 1.347; 95%CI:1.036–1.750), low self-esteem (AOR = 2.020; 95%CI:1.578–2.585), low family support (AOR = 3.532; 95%CI:2.486–5.017), loneliness (AOR = 1.611; 95%CI:1.211–2.143), depression (AOR = 4.882; 95%CI = 3.861–6.175), and hopelessness (AOR = 1.602; 95%CI:1.154–2.224). Nagelkerke R square was 0.364 indicating the regression model explained 36.4% of variance in suicidality.

Conclusions

Our study revealed several significant predictors of suicidality among adolescents which can be targeted to develop suicide prevention strategies.

Introduction

Suicide is when someone dies because they intentionally kill themselves, whereas suicidality is a term frequently used to describe a variety of suicidal behavior including suicidal ideation, plans, threats, attempts, and complete suicide [1, 2]. Suicidal ideation, plans, threats, and attempts considered as non-fatal suicidality, whereas complete suicide considered as fatal suicidality because it result in death [2].

Suicide is a major global health problem and has become the second leading cause of death among adolescents between 10 to 19 years of age [3]. The World Health Organization (WHO) estimated that the suicide rate for this age group from 2010 to 2016 was 3.77/100,000 people [4]. A recent study found that the global 12-month pooled prevalence of suicidal ideation among adolescents was 14.0% [5].

While early childhood is a relatively low-risk period for suicide, starting from early adolescence onward, the likelihood of this life-threatening behavior rises significantly [4, 6]. Suicidality can occur in adolescence even without underlying psychiatric illness [7]. Moreover, despite ranking among the high suicide-risk groups, adolescents are also renowned as poor help-seekers, which can contribute to their delayed care and unmet needs. For example, one recent study found that adolescents who were at risk for suicide saw a roughly one-year delay in seeking medical attention following their first attempted suicide [8], which underscores the need for more proactive detection and prevention strategies for this vulnerable and sometimes marginalized, at-risk group.

Suicidality is a complex multi-factorial phenotype [9]. This life-threatening behavior is considered the end-point of a complex interaction involving genetic factors, including Single Nucleotide Polymorphism (SNP) in the DNA sequence of numerous protein-coding genes within the human genome such as BDNF, NTRK2, NLGN1, SOX5, THP1, 5-HTT [10, 11]; among others, and non-genetic factors including individual psychological-psychopathological characteristics [9]. Previous studies highlighted various risk factors of suicidality among adolescents, including dissatisfied grade results, poor social support [1, 12], alcohol use [13, 14], loneliness [5, 14, 15], depressive symptoms [16,17,18,19], hopelessness [16, 20, 21], low resilience [16, 22, 23], female gender and cigarette smoking [1, 14].

High-income countries have conducted extensive research on suicidal behaviors. However, there is still limited evidence on this topic in low-income and middle-income countries (LMICs) [17]. It is estimated that 75% of global suicides occur in LMICs, but the suicide data in these countries remains under-reported [1]. Indonesia, a predominately Muslim LMIC in Southeast Asia and regarded as the fourth most populous country in the world, currently suffers from under-reporting regarding the suicide rate data due to poor suicide data registry in the country [24]. According to recent WHO classifications, Indonesia's suicide data registry has the lowest quality score, suggesting unreliable and less valid data. The reported data from 2016 to 2018 revealed that the suicide rate in Indonesia ranged from 46.65 to 52.10 per 100,000 deaths, and it is estimated that the suicide deaths could be as much as four-fold higher than that reported [24].

In Indonesia, the Global School-Based-Students Health Survey (GSHS) was conducted in 2015 to assess the prevalence of suicidality among adolescents [1, 14]. However, more recent research to explore possible risk factors and protective factors among adolescents in Indonesia is still limited. Moreover, suicide prevention programs specifically targeting these possible protective and risk factors of suicidality for adolescents in this region remain inadequate [1, 17]. Considering the insufficient evidence regarding this serious problem in Indonesia, the purpose of this study was to investigate the prevalence and risk factors of suicidality in Indonesian adolescents. Since adolescence is also considered a critical period for early detection and intervention to prevent suicidal behavior [6], the identification of risk and protective factors for suicidal behaviors in adolescents is urgently needed. Understanding the risk and protective factors of suicidality among adolescents would serve as an important ‘stepping stone’ toward the development of novel early detection strategies to identify adolescents who are at risk for suicide and the implementation of more effective and proactive suicide prevention strategies for these vulnerable populations in the country.

Methods

Study design, setting, and period

This cross-sectional study was conducted at 15 public high schools across 4 provinces in Java Island, Indonesia: West Java, Central Java, Yogyakarta, and East Java. The data were collected in an 12 months period from January until December 2023.

Sample size calculation and sampling method

Calculation using the logistic regression rule of thumb equation was performed to determined minimum sample size for this study, described as n = 100 + 50(i) where i refers to the number of independent variables in regression model [16]. Based on the assumption above, the minimum sample size was n = 100 + 50(14) = 800. The participants were recruited using consecutive sampling, and the eligibility criteria for this study were: high school students aged 14 to 18 years old, had academic grade of 10 until 12, using WhatsApp as a means of communication, and agreed to voluntarily participate by signing the informed consent form.

Data collection

The academic affairs office of each high school provided the student demographic data, including their name, students ID number, academic grade, sex, age, and WhatsApp number, that were used to identify the students who met the eligibility criteria. The eligible students were then contacted by the researchers via WhatsApp and explained about the study information. After this recruitment step, the researchers asked for their willingness to participate in the study. If they consented to participate, the researchers then made a schedule for when the data collection process would be done, and then the researchers made an appointment with the participants based on the previously made schedule.

The data collection process was carried out through a face-to-face interview. To ensure the participants privacy, confidentiality, and comfort when they fill out the study questionnaires, the data collection process was conducted in the consultation room of the Student's Guidance and Consultation Unit in each high school. Additionally, to further provide privacy and comfort to participants, there was only one participant and one researcher in the room at a time. Firstly, the researchers once again explained the study information to the participants, and then the researchers asked them whether they were still willing to voluntarily participate in the study. If they consented to participate, they were asked to sign the informed consent form as a prerequisite to participating in the study. After participants signed the informed consent, the researchers explained to them the information about each study questionnaire and how those questionnaires were filled out. If the participants understood, they were asked to fully complete the study questionnaires. The researchers then left the participants alone and stayed a short distance away during the process of filling out the questionnaires, so that the researchers would be available if there were questions from the participants. Participants are able to reach the researchers with any questions they may have while completing the questionnaires, and the researchers will be delighted to answer them. After the participants completed the questionnaires, the researchers checked them out, and if there was an incomplete questionnaire, the researchers asked the participants to complete it. This procedure was repeated to recruit participants until the end of the data collection period.

Instruments

The data was collected using a sociodemographic questionnaire and seven standardized self-rated instruments in Indonesian language that have been validated in previous studies.

A sociodemographic profile questionnaire was administered to assess several participant’s information, including sex, age, academic grade, origin of school, religion, monthly family income, financial issues, smoking status, alcohol consumption status, participation in extracurricular activities, and satisfaction with grade point average (GPA).

Self-esteem among high school students was assessed using the Rosenberg Self-Esteem Scale (RSES). The RSES is a self-rated scale that is widely used to examine self-esteem among clinical and non-clinical samples, both in adults and adolescents [25], and this instrument consists of 10 items with 5 items being favorable statements and 5 items being unfavorable statements. Each item has four possible answers and is ranked using a Likert scale [26]. Items 2, 5, 6, 8, and 9 were considered as unfavorable statements, and the scores ranged from strongly agree (1 point) to strongly disagree (4 points). Items 1, 3, 4, 7, and 10 were considered as favorable statements and the scoring were reversed [26]. The RSES total scores can be obtained by adding the scores from each item, which range from 10 to 40 and are classified into 3 levels based on the total score obtained by the participants: low (10–25), moderate (26–29), and high self-esteem (30–40) [26]. Previous study provided evidence that the Indonesian version of this instrument is valid and reliable to assess self-esteem among general population with Cronbach’s ɑ = 0.82 [27]. Moreover, this instrument has also been validated among Indonesian adolescents and showed high internal reliability with Cronbach’s ɑ = 0.899 [28].

Loneliness among high school students was assessed using the UCLA Loneliness Scale Three-Item (ULS-3). This instrument is a self-rated scale that is widely used to examine degree of loneliness among adolescents [29]. ULS-3 consists of three items and each item had three possible answers which are ranked on a Likert scale as follows: hardly ever (1 point), some of the time (2 point), and often (3 point). The ULS total scores can be obtained by adding the scores from each item. The ULS-3 total score ranges between 3–9 with higher scores representing higher loneliness level [30]. Based on the total score obtained by participants, loneliness is categorized into two groups: lonely (≥ 6), and not lonely (< 6). A previous study validated the Indonesian version of ULS-3 among Indonesian adolescents and provided evidence that the Indonesian version of this instrument is valid and reliable to assess loneliness among adolescents with Cronbach’s ɑ = 0.81 [31].

The social support perceived by high school students was rated using the Multidimensional Scale of Perceived Social Support (MSPSS). Originally developed to assess perceived social support among university students and adolescents [32], MSPSS has since been widely used to examine the social support perceived by adolescents [33]. This instrument comprise of 12 items and each item had 7 possible answers rated using a Likert scale ranging from “very strongly disagree” (1 point) to “very strongly agree” (7 points). The MSPSS is also divided into three domain that represent the source of social support: Family support consisting of 4 items (items 3, 4, 8, and 11), Friends support consisting of 4 items (items 6, 7, 9, and 12), and Significant Others support consisting of 4 items (items 1, 2, 5, and 10). The MSPSS total scores, which range from 12 to 84, can be obtained by adding the scores from each item, and better scores imply better social support as reported by a person. Moreover, by summing the items in each of the three domain and dividing the result by 4, one can find the social support score on each of the three domains [34]. The social support from each domain then can be categorized into three levels: low support (1–2.9), moderate support (3–5), and high support (5.1–7) [35]. A previous study validated the Indonesian version of MSPSS among Indonesian adolescents and provided evidence that the Indonesian version of this instrument is valid and reliable with Cronbach ɑ of 0.81 for the Family domain, 0.82 for the Friend’s domain, and 0.75 for the Significant others domain [34].

To assess hopelessness among high school students, the Beck Hopelessness Scale (BHS) was administered. The BHS is a standardized self-rated instrument widely validated and used as a tool to examine hopelessness in the general population as well as adolescents, both in clinical and non-clinical settings [36]. The BHS consists of 20 items (11 negative statements and 9 positive statements) and each item has two possible answers: true/false. Items with negative statements were scored as follows: true (1 point) and false (0 point), whereas the items with positive statements were reverse scored. The BHS total scores, which range from 0 to 20, can be obtained by adding the scores from each item, with higher scores representing a higher severity of hopelessness [2]. The BHS total score ≥ 9 was used as the cut off-point to determine the hopelessness status among participants [2]. The Indonesian version of the BHS is considered to be a valid and reliable instrument for assessment of hopelessness among general population (Cronbach’s ɑ = 0.918) [37]. Moreover, this instrument has also been validated among Indonesian adolescents and showed high internal reliability with Cronbach’s ɑ = 0.867 [38].

The 9-Item Patient Health Questionnaire (PHQ-9) was used to assess depression symptoms among high school students [2]. The PHQ-9 is a standardized self-rated instrument widely validated and used as a tool for screening for depression in different age groups including adolescents, both in clinical and non-clinical settings [39]. The PHQ-9 consists of nine items and each item has four answer choices rated using a Likert scale ranging from 0 (never at all) to 3 (almost every day). The PHQ-9 total scores, which range from 0 to 27, can be obtained by adding the scores from each item, with higher scores indicate more severity of depression. The PHQ-9 total score that each participant received was further categorized into two groups: normal (< 10) and depression (≥ 10) [2]. A previous study provided evidence that the Indonesian version of this instrument is valid and reliable to assess depression symptoms among university students with Cronbach’s ɑ = 0.89 [40]. Moreover, this instrument has also been validated among Indonesian adolescents and showed robust internal reliability with Cronbach’s ɑ = 0.777 [41].

Resilience among high school students was measured using the 10-Items of the Connor-Davidson Resilience Scale (CD-RISC-10). The CD-RISC-10 is a standardized self-rated instrument widely validated and used as a tool to examine psychological resilience in different age groups of populations including adolescents, both in clinical and non-clinical settings [42, 43]. The CD-RISC-10 consists of 10 items with five possible responses rated using a Likert scale ranging from “not true at all” (0 point) to “true nearly all the time” (4 points) [44]. The total scores of this instrument, which range from 0 to 40, can be obtained by adding the scores from each item, with higher scores representing higher resilience [44]. Furthermore, the psychological resilience level was categorized into two groups based on the total score obtained by participants: high resilience (≥ 25.5), and low resilience (< 25.5) [44]. A previous study provided evidence that the Indonesian version of this instrument is valid and reliable to assess the resilience level in general population with Cronbach’s ɑ = 0.868 [45]. Additionally, this instrument has also been validated among Indonesian adolescents and showed high internal reliability with Cronbach’s ɑ = 0.919 [46].

To assess suicidality among high school students, the Suicidal Behaviors Questionnaire-Revised (SBQ-R) was administered. The SBQ-R is a standardized self-rated instrument widely validated and used as a tool for screening the presence of suicidal behavior in different age groups including adolescents, both in clinical and non-clinical settings [47, 48]. This instrument consists of four items rated using Likert-scale [2]. The first item has six possible responses to determine lifetime suicide ideation and/or suicide attempts. The second item has five possible responses to determine 12-months suicide ideation. The third item has five possible responses to determined lifetime suicide threat. The last item has seven possible responses to determine the future probability of conducting suicide. The total score ranges from 3 to 18 and was categorized into two groups: high suicidality (≥ 7), and low suicidality (< 7) [2]. Previous study provided evidence that the Indonesian version of this instrument is valid and reliable to assess suicidality among general population with Cronbach’s ɑ = 0.760 [49]. Moreover, this instrument has also been validated among Indonesian adolescents and showed high internal reliability with Cronbach’s ɑ = 0.89 [50].

Ethical considerations

The ethical clearance (number: 005.3/FIKES/PL/I/2023) was granted by the Institutional Review Board of Universitas Respati Yogyakarta, Indonesia on January 27, 2023. Before the data collection, detailed research information was provided to the participants and the informed consent was obtained from them before commencing the study. The participant's identity and their data were kept confidential throughout the study, with only the researcher having access to the study data.

Statistical analysis

The Windows Version of SPSS 24 (IBM Corp, Armonk, NY) was used as software to perform statistical analysis. Bivariate analysis utilizing Chi-square test was employed for all independent variables to obtain p-value and crude odds ratio (COR) along with its associated 95% confidence interval (CI). Binary logistic regression analysis was performed as multivariable analysis to obtain p-value and adjusted odds ratio (AOR) along with its associated 95% confidence interval (CI). The p-value < 0.05 was considered statistically significant. Independent variables with p < 0.25 in the Chi-square tests were included in the logistic regression analysis. To test the goodness of fit in the logistic regression model, Hosmer and Lemeshow tests were employed. The Nagelkerke R Square value was obtained to analyze the extent to which the whole model accounted for the variance observed in the dependent variable.

Results

Sociodemographic profiles of the participants

In this study, 2317 high school students who meet the eligibility criteria voluntarily participated and their sociodemographic data are provided in Table 1. The average age of the participants was 16.30 years with standard deviation (SD) of 0.98 and ranged between 14 to18 years. Most participants are female (n = 1375; 59.3%). In the Indonesian education system, high school education program is a 3-year program and divided into 3 academic grades (grade 10 to 12). Most of the high school students who participated in this study were grade 10 students (n = 1016; 43.8%). Based on their religion, most of the participants are Muslim (n = 2024; 87.4%). Most of the participants self-reported a monthly family income above IDR 3 million (n = 576; 24.9%) and indicted they did not have financial difficulties (n = 1284; 55.4%). The majority of the high school students participated in some extracurricular activities (n = 1556; 67.2%) and were dissatisfied with their current academic achievement (n = 1525; 65.8%). Only 4.2% of the students reported they were smoking, and 1.6% reported consuming alcohol. As many as 56 students (2.4%) reported that they had a chronic illness.

Table 1 Sociodemographic characteristics of the participating high school students (n = 2317)

Profile of major individual psychological predictor variables

Table 2 shows the detailed information about the individual psychological predictor variables assessed in this study. Most of the participants had high resilience (n = 1245; 53.7%) and moderate self-esteem (n = 1045; 45.1%). Based on the source of social support, almost half of the participants reported receiving high Family support (n = 1147; 49.5%), while reported moderate Friends support (n = 1096; 47.3%) and Significant others support (n = 975; 42.1%). The prevalence of loneliness and depression among students were surprisingly high (70.1% and 38.9%, respectively), whereas the prevalence of hopelessness among students were 11.4%.

Table 2 Profile of major individual psychological independent variables (n = 2317)

Suicidality among high school students in Indonesia

Table 3 shows the trends of suicidal behavior among high school students as determined by the SBQ-R. Our study indicated that 601 (25.9%) of the high school students had high suicidality. In their lifetime, 26.5% of the participants had a suicide ideation, 18.2% had suicide plans, 14.1% had a suicide threat, and 4.4% had suicide attempts. It should be noted that logically suicide ideation is also present among the participants with suicide planning, threats, and attempts. In the last 12-months period, 998 (43.1%) of participants reported that they had suicide ideation.

Table 3 Suicidal behavior among high school students assessed using SBQ-R (n = 2317)

Predictors of suicidality among participating high school students

We performed binary logistic regression as multivariable analysis to determine the significant predictors of suicidality among high school students, and 14 independent variables with p values < 0.25 in the Chi-square tests were included. Table 4 provides more detailed information regarding the results of the regression analysis. Among the participating high school students, significant associations were found with suicidality and the following risk factors: female student (AOR = 1.912; p = 0.001; 95%CI:1.507–2.425), chronic illness (AOR = 2.886; p = 0.001; 95%CI:1.545–5.389), low resilience (AOR = 1.347; p = 0.026; 95%CI:1.036–1.750), low self-esteem (AOR = 2.020; p = 0.001; 95%CI:1.578–2.585), low family support (AOR = 3.532; p = 0.001; 95%CI:2.486–5.017), loneliness (AOR = 1.611; p = 0.001; 95%CI:1.211–2.143), depression (AOR = 4.882; p = 0.001; 95%CI = 3.861–6.175), and hopelessness (AOR = 1.602; p = 0.005; 95%CI:1.154–2.224). The AOR value indicated that depression acted as the strongest predictor of suicidality. The Nagelkerke R Square value of 0.364 indicated that the binary logistic regression model explained 36.4% of suicidality. The Hosmer–Lemeshow test with p = 0.341 indicated the goodness of fit of the model.

Table 4 Predictors of suicidality among high school students based on binary logistic regression (n = 2317)

Discussion

Our study elucidated that the prevalence of lifetime suicide ideation was 26.5%, while the 12‐months suicide ideation was 43.1%, lifetime suicide plans was 18.2%, lifetime suicide threats was 14.1%, and lifetime suicide attempts was 4.4%. The adolescent’s suicidality found in our study is remarkably higher compared to the findings from a previous study conducted in Indonesia which ranged between 4.75% and 5.2% for suicidal ideation, 2.46% and 2.7% for suicide attempt, and 5.6% for suicidal plans [1, 14]. This remarkable disparage may be due to the differences in study settings and participants as well as unprecedented personal, social, and economical disruption derived from the COVID-19 pandemic when global tragedy suddenly struck fear into everyone, which impacted on adolescent’s psychological well-being [31]. Those previous studies used secondary data from the Indonesian GSHS in 2015. GSHS was conducted among junior and senior high school students from 75 schools located in Sumatera and Java as well as other regions [14]. This current study only involved senior high school students as participants and was also conducted in Yogyakarta, a province with a high suicide rate in Indonesia [24], which was not originally included in the GSHS [14]. It is shown in several studies that the risk of suicide dramatically increases from early to late adolescence and early adulthood [4, 6]. Empirical evidence also indicated that the prevalence of adolescent suicidality was highly varied in different study settings [5]. For example, one recent study of variations in global suicide trends found that Asia had the lowest pooled prevalence of suicidal ideation (8%) while the highest was in Africa (21%) [5]. This variability was also observed in other studies in the prevalence of suicide ideation (ranging from 0.9% – 38.7%), suicide plans (5.5% – 8.7%), and suicide attempts (0.9% – 20.5%) among different study settings [51].

Our study found that 8 out of 18 independent variables acted as significant risk factors of suicidality among Indonesian adolescents, indicating the complex nature of this life-threatening behavior. Moreover, some of our predictors were not identified in previous studies conducted in Indonesia [1, 14], such as chronic illness, low resilience, low self-esteem, low family support, depression, and hopelessness. Our findings provide additional evidence regarding the risk factors of adolescent suicidality in this country. Based on the Nagelkerke R square value, our identified predictors only contributed to 36.4% in explaining the variance of suicidality, suggesting there are other factors that contribute to suicidality among Indonesian adolescents that still have to be identified. It is well-established that suicidality is complex and multi-factorial phenotype involving both endogenous and exogenous risk factors [9].

Our study found that sex is the only sociodemographic variable that had a significant association with suicidality, with females having 1.912 times higher risk of suicidality compared to males. The higher risk of suicidal behaviors among adolescent girls is consistent with a previous study [52]. Our findings also support the results from a previous study that reported the higher risk of suicidality among Indonesian adolescent girls [1, 14]. The prevalence of suicidality among adolescent girls is shown to significantly increase after the transition to puberty [53]. Female youths tend to have higher levels of depression and hopelessness and these predictor risk factors are known to contribute to higher suicidal ideation among females [52].

Empirical evidence indicated that chronic medical conditions had a significant association with increased risk for the suicidal ideation, plans, and attempts in adolescents [54, 55]. Consistently, our study demonstrated that chronic illness was a significant predictor of suicidality among adolescents, indicated by those participants with a chronic medical condition having 2.886 fold increased risk of suicidality compared to their counterparts. Long-term medical issues increased the likelihood of depression in adolescents, which might result in suicide thoughts and attempts [56]. The incurable state of their disease, the symptoms and treatment are potentially significant sources of stress. Perceived as a burden to their lives, they did not know how to manage their disease properly. Research has shown that physical deterioration combined with inability to cope adaptively with an illness and its symptoms could lead to suicidality [57].

Our study demonstrated that self-esteem had a significant association with suicidality, indicated by adolescents with low self-esteem are 2.020 times more likely to experience suicidality than their counterparts. In line with our findings, low self-esteem has been consistently identified among significant risk factors for suicidal behaviors among adolescents [58]. Contrarily, having a high level of self-esteem protects someone from suicidal behavior [59]. Adolescents with low self-esteem tended to have increased suicidal ideation and had a greater risk to commit suicide attempts [58]. Furthermore, depression and hopelessness, two of the most prominent risk factors for suicide, cannot fully explain the substantial variation in suicide ideation that low self-esteem accounts for [58].

Our study elucidated that loneliness, a condition in which someone perceives a disparity between their expected social relationships and their actual experiences [60], acted as a significant predictor of suicidality, in which adolescents who felt lonely had a 1.611 fold increased risk of suicidality compared to their counterparts. Consistently, previous studies showed that adolescents who felt lonely have a higher risk of suicidal behaviors [5, 15]. Our findings also support the results from previous studies that reported loneliness was a significant risk factor of suicidality among Indonesian adolescents [1, 14].

Our study provides evidence regarding the role of depression as the most robust predictor of suicidality among adolescents, indicated by adolescents with depression having 4.882 fold increased risk of suicidality compared to non-depressed adolescents. This finding was consistent with a previous study that reported the strong positive association between depression and suicidal behaviors such as suicidal ideation [16, 18] and suicidal attempts [19]. Our findings also revealed that the high prevalence of depression among adolescents (38.9%) could have contributed to the higher prevalence of suicidal ideation found in our study compared to the previous research in Indonesia [1, 14]. The onset of depression commonly appears in adolescence and a recent meta-analysis identified depression to be a robust predictor of suicidal ideation among adolescents [18]. Depression possess numerous detrimental symptoms and in its most extreme form, it can lead to life threatening behaviors such as self-harm and suicide. Numerous studies have demonstrated that depression, particularly when coupled with hopelessness, is the most reliable indicator of suicide risk [16].

Based on the source of social support perceived by participants, our study elucidated that Family support is the only source of social support that has a significant association with suicidality, in which participants with low perceived Family support are 3.532 times more likely to experience suicidality than their counterparts. Empirical evidence emphasizes the important role of family factors on adolescent suicidal behaviors [12, 59]. It has been shown that negative relationships with parents, low parental concern and dysfunctional family interactions were significantly associated with greater suicide risk among adolescents [61,62,63], and this relationship were mediated by depression [61]. Similarly, another study discovered a negative relationship between family support and adolescent suicidal thoughts, depressive symptoms, and hopelessness [63].

Our study found that resilience, the individual’s capacity to perform positive adaptation and ‘bounce back’ when facing hardships in their life, traumatic experiences, and stressful life events [44], acted as significant predictor of suicidality. Our study revealed that the risk of suicidality was 1.347 times higher in adolescents with low resilience than in those with high resilience. Previous studies demonstrated the protective role of resilience against suicidal behavior, with the low psychological resilience presenting as a risk factor for suicidal behavior [16, 22, 23]. It is well established that individuals who possess strong psychological resilience have the ability to preserve their positive mental health during adversity, trauma, and stressful events [44].

Our study identified that hopelessness had a significant association with suicidality among adolescents. The probability of suicidality was 1.602 fold higher in adolescents who felt hopeless than in those who did not. Hopelessness, defined as an individual’s negative perceptions regarding their future [64], is consistently identified as a risk factor for suicidality, especially when combined with depression [20]. Consistently, previous studies demonstrated the strong positive association between hopelessness and suicidal ideation and behavior [16, 20, 21]. Hopelessness has a pivotal role in the trajectory from depression to suicide [64, 65].

Our study possesses several limitations that should be considered. First, since this was a cross-sectional study, the nature of this research design make it impossible to show a cause-and-effect association between the variables. The findings of this study support the recommendation for longitudinal research to be conducted in the future to provide better understanding regarding our identified predictors. Second, since we adopted consecutive sampling—a type of non-probability sampling strategy, to recruited the participants, the findings of our study had a limited generalizability due to the nature of the sampling strategy used. The findings of this study support the recommendation for further research to be conducted in the future with multi-stage stratified random sampling approach to retrieve nationally representative sample and to elevate the generalizability of the study conclusion. Third, the utilization of self-reporting questionnaires as instrument to measures study variables in this study may have raised the possibility of recall bias and social desirability bias. Mixed-methods research should be conducted in the future to obtain qualitative responses from the participants that could be used as supplementary information.

Conclusions

Our study provides additional evidence supporting the complex multi-factorial nature of suicidality among adolescents and identified depression as the strongest predictor of this life-threatening behavior. Our findings underscore the needs of strengthening the mental health care services for high school institutions and supporting more pro-active detection and suicide prevention strategies for at-risk adolescents. Assessments that target the significant predictors identified in this study should be performed on a regular basis as an early detection strategy. Special attention should also be provided for female adolescents and those with chronic medical conditions. Our findings also support the development of more pro-active interventions that target the identified predictors as suicide prevention strategies among adolescents in Indonesia.

Availability of data and materials

Data will be available on reasonable request through contacting the corresponding author if needed.

Abbreviations

WHO:

World Health Organization

SNP:

Single Nucleotide Polymorphism

DNA:

Deoxyribonucleic Acid

BDNF:

Brain-Derived Neurotrophic Factor

NTRK2:

Neurotrophic Receptor Tyrosine Kinase 2

NLGN1:

Neuroligin 1

SOX5:

SRY-Box Transcription Factor 5

THP1:

Tryptophan Hydroxylase-1

5-HTT:

Serotonin Transporter

LMIC:

Lower Middle Income Country

RSES:

Rosenberg Self-Esteem Scale

ULS-3:

UCLA Loneliness Scale Three-Item

MSPSS:

Multidimensional Scale of Perceived Social Support

BHS:

Beck Hopelessness Scale

PHQ-9:

The 9-Item Patient Health Questionnaire

CD-RISC-10:

The 10-Items of the Connor-Davidson Resilience Scale

SBQ-R:

Suicidal Behaviors Questionnaire-Revised

GSHS:

The Global School Based-Students Health Survey

References

  1. Marthoenis M, Yasir Arafat SM (2022) Rate and Associated Factors of Suicidal Behavior among Adolescents in Bangladesh and Indonesia: Global School-Based Student Health Survey Data Analysis. Scientifica 5:2022. https://doi.org/10.1155/2022/8625345

    Article  Google Scholar 

  2. Tan ST, Sherina MS, Rampal L, Normala I (2015) Prevalence and predictors of suicidality among medical students in a public university. Med J Malaysia 70(1):1–5

    CAS  PubMed  Google Scholar 

  3. Hink AB, Killings X, Bhatt A, Ridings LE, Andrews AL (2022) Adolescent suicide—Understanding unique risks and opportunities for trauma centers to recognize, intervene, and prevent a leading cause of death. Curr Trauma Rep 8(2):41–53. https://doi.org/10.1007/s40719-022-00223-7

    Article  PubMed  PubMed Central  Google Scholar 

  4. Glenn CR, Kleiman EM, Kellerman J, Pollak O, Cha CB, Esposito EC, Porter AC, Wyman PA, Boatman AE (2020) Annual Research Review: A meta-analytic review of worldwide suicide rates in adolescents. J Child Psychol Psychiatry 61(3):294–308. https://doi.org/10.1111/jcpp.13106

    Article  PubMed  Google Scholar 

  5. Biswas T, Scott JG, Munir K, Renzaho AM, Rawal LB, Baxter J, Mamun AA (2020) Global variation in the prevalence of suicidal ideation, anxiety and their correlates among adolescents: a population based study of 82 countries. EClinicalMedicine 1:24. https://doi.org/10.1016/j.eclinm.2020.100395

    Article  Google Scholar 

  6. Cavelti M, Kaess M (2021) Adolescent suicide: an individual disaster, but a systemic failure. Eur Child Adolesc Psychiatry 30(7):987–990. https://doi.org/10.1007/s00787-021-01834-2

    Article  PubMed  PubMed Central  Google Scholar 

  7. Becker M, Correll CU (2020) Suicidality in childhood and adolescence. Dtsch Arztebl Int 117(15):261. https://doi.org/10.3238/arztebl.2020.0261

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lustig S, Koenig J, Resch F, Kaess M (2021) Help-seeking duration in adolescents with suicidal behavior and non-suicidal self-injury. J Psychiatr Res 1(140):60–67. https://doi.org/10.1016/j.jpsychires.2021.05.037

    Article  Google Scholar 

  9. Osipova NN, Dmitrieva EV, Beglyankin NI, Bardenshteyn LM (2020) Predictors of Suicidal Behavior in Adolescents with Depressive Disorders. Neurosci Behav Physiol 50:40–44. https://doi.org/10.1007/s11055-019-00866-1

    Article  Google Scholar 

  10. Li QS, Shabalin AA, DiBlasi E, Gopal S, Canuso CM, FinnGen, International Suicide Genetics Consortium, Palotie A, Drevets WC, Docherty AR, Coon H (2023) Genome-wide association study meta-analysis of suicide death and suicidal behavior. Mol Psychiatry 28(2):891–900. https://doi.org/10.1038/s41380-022-01828-9

    Article  CAS  PubMed  Google Scholar 

  11. Mirkovic B, Laurent C, Podlipski MA, Frebourg T, Cohen D, Gerardin P (2016) Genetic association studies of suicidal behavior: a review of the past 10 years, progress, limitations, and future directions. Front Psychiatry 23(7):158. https://doi.org/10.3389/fpsyt.2016.00158

    Article  Google Scholar 

  12. Alvarez-Subiela X, Castellano-Tejedor C, Villar-Cabeza F, Vila-Grifoll M, Palao-Vidal D (2022) Family factors related to suicidal behavior in adolescents. Int J Environ Res Public Health 19(16):9892. https://doi.org/10.3390/ijerph19169892

    Article  PubMed  PubMed Central  Google Scholar 

  13. Rizk MM, Herzog S, Dugad S, Stanley B (2021) Suicide risk and addiction: the impact of alcohol and opioid use disorders. Curr Addict Rep 8:194–207. https://doi.org/10.1007/s40429-021-00361-z

    Article  PubMed  PubMed Central  Google Scholar 

  14. Putra IG, Karin PA, Ariastuti NL (2021) Suicidal ideation and suicide attempt among Indonesian adolescent students Int J Adolesc Med Health 33(5) https://doi.org/10.1515/ijamh-2019-0035

  15. McClelland H, Evans JJ, Nowland R, Ferguson E, O’Connor RC (2020) Loneliness as a predictor of suicidal ideation and behaviour: a systematic review and meta-analysis of prospective studies. J Affect Disord 1(274):880–896. https://doi.org/10.1016/j.jad.2020.05.004

    Article  Google Scholar 

  16. Fitriawan AS, Setyaningsih WA, Wulandari AN, Samutri E, Achmad BF, Budiyati GA, Nailufar Y, Retnaningsih LN (2023) Prevalence and predictors of suicidality among nursing students in Indonesia. KONTAKT 25(1) https://doi.org/10.32725/kont.2023.009

  17. Nguyen Thi Khanh H, Nguyen Thanh L, Pham Quoc T, Pham Viet C, Duong Minh D, Le Thi Kim A (2020) Suicidal behaviors and depression “among adolescents in Hanoi, Vietnam: A multilevel analysis of data from the Youth Risk Behavior Survey 2019. Health Psychol Open. 7(2):2055102920954711. https://doi.org/10.1177/2055102920954711

    Article  PubMed  PubMed Central  Google Scholar 

  18. May AM, Klonsky ED (2016) What distinguishes suicide attempters from suicide ideators? A meta-analysis of potential factors. Clin Psychol: Sci Pract 23(1):5. https://doi.org/10.1111/cpsp.12136

    Article  Google Scholar 

  19. Abdul Aziz FA, Abd Razak MA, Ahmad NA, Awaluddin SM, Lodz NA, Sooryanarayana R, Shahein NA, Mohamad Kasim N, Abd Wahab NA, Jamaluddin R (2019) Factors Associated With Suicidal Attempt Among School-Going Adolescents in Malaysia. Asia Pac J Public Health 31(8_suppl):73S–S79. https://doi.org/10.1177/1010539519862161

    Article  PubMed  Google Scholar 

  20. Wolfe KL, Nakonezny PA, Owen VJ, Rial KV, Moorehead AP, Kennard BD, Emslie GJ (2019) Hopelessness as a predictor of suicide ideation in depressed male and female adolescent youth. Suicide Life Threat Behav 49(1):253–263. https://doi.org/10.1111/sltb.12428

    Article  PubMed  Google Scholar 

  21. Horwitz AG, Berona J, Czyz EK, Yeguez CE, King CA (2017) Positive and negative expectations of hopelessness as longitudinal predictors of depression, suicidal ideation, and suicidal behavior in high-risk adolescents. Suicide Life Threat Behav 47(2):168–176. https://doi.org/10.1111/sltb.12273

    Article  PubMed  Google Scholar 

  22. Stark L, Seff I, Yu G, Salama M, Wessells M, Allaf C, Bennouna C (2022) Correlates of suicide ideation and resilience among native-and foreign-born adolescents in the United States. J Adolesc Health 70(1):91–98. https://doi.org/10.1016/j.jadohealth.2021.07.012

    Article  PubMed  Google Scholar 

  23. Han J, Wong I, Christensen H, Batterham PJ (2022) Resilience to suicidal behavior in young adults: a cross-sectional study. Sci Rep 12(1):11419. https://doi.org/10.1038/s41598-022-15468-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Onie S, Usman Y, Widyastuti R, Lusiana M, Angkasawati TJ, Musadad DA (2022). Indonesia’s first suicide statistics profile: an analysis of suicide and attempt rates, underreporting, geographic distribution, gender, method, and rurality. Lancet. 8. https://doi.org/10.1016/j.lansea.2023.100245

  25. Wood C, Griffin M, Barton J, Sandercock G (2021) Modification of the Rosenberg Scale to Assess Self-Esteem in Children. Front Public Health 17(9):655892. https://doi.org/10.3389/fpubh.2021.655892

    Article  Google Scholar 

  26. García JA, y Olmos FC, Matheu ML, Carreño TP (2019). Self esteem levels vs global scores on the Rosenberg self-esteem scale. Heliyon. 5(3). https://doi.org/10.1016/j.heliyon.2019.e01378

  27. Achmad BF, Fitriawan AS, Kurniawan D, Chen HM (2023). Mediating effect of self-esteem on the relationship between academic self-efficacy and depression symptoms among nursing students participating in blended learning. Heliyon. 9(11). https://doi.org/10.1016/j.heliyon.2023.e22526

  28. Sarfika R, Moh Yanuar Saifudin IM, Sari IM, Murni D, Malini H, Abdullah KL (2023) Investigating associations between emotional and behavioral problems, self-esteem, and parental attachment among adolescents: A cross-sectional study in Indonesia. Heliyon. 9(11):e21459. https://doi.org/10.1016/j.heliyon.2023.e21459

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cole A, Bond C, Qualter P, Maes M (2021) A Systematic Review of the Development and Psychometric Properties of Loneliness Measures for Children and Adolescents. Int J Environ Res Public Health 18(6):3285. https://doi.org/10.3390/ijerph18063285

    Article  PubMed  PubMed Central  Google Scholar 

  30. Liu T, Lu S, Leung DK, Sze LC, Kwok WW, Tang JY, Luo H, Lum TY, Wong GH (2020) Adapting the UCLA 3-item loneliness scale for community-based depressive symptoms screening interview among older Chinese: a cross-sectional study. BMJ Open 10(12):e041921. https://doi.org/10.1136/bmjopen-2020-041921

    Article  PubMed  PubMed Central  Google Scholar 

  31. Liem A, Prawira B, Magdalena S, Siandita MJ, Hudiyana J (2022) Predicting self-harm and suicide ideation during the COVID-19 pandemic in Indonesia: a nationwide survey report. BMC Psychiatry 22(1):1. https://doi.org/10.1186/s12888-022-03944-w

    Article  CAS  Google Scholar 

  32. Zimet GD, Dahlem NW, Zimet SG, Farley GK (1988) The multidimensional scale of perceived social support. J Pers Assess 96(1):103–112. https://doi.org/10.1080/00223891.2013.838170

    Article  Google Scholar 

  33. Merino-Soto C, Boluarte Carbajal A, Toledano-Toledano F, Nabors LA, Núñez-Benítez MÁ (2022) A New Story on the Multidimensionality of the MSPSS: Validity of the Internal Structure through Bifactor ESEM. Int J Environ Res Public Health 19(2):935. https://doi.org/10.3390/ijerph19020935

    Article  PubMed  PubMed Central  Google Scholar 

  34. Laksmita OD, Chung MH, Liao YM, Chang PC (2020) Multidimensional Scale of Perceived Social Support in Indonesian adolescent disaster survivors: A psychometric evaluation. PLoS ONE 15(3):e0229958. https://doi.org/10.1371/journal.pone.0229958

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Samson P (2020) Effect of perceived social support on stress, anxiety and depression among Nepalese nursing students. Indian J Cont Nsg Edn 21(1):59–63. https://doi.org/10.4103/ijcn.ijcn_8_20

    Article  Google Scholar 

  36. Lester D (2015) Hopelessness in adolescents. J Affect Disord 1(173):221–225. https://doi.org/10.1016/j.jad.2014.10.048

    Article  Google Scholar 

  37. Hutajulu JM, Djunaidi A, Triwahyuni A (2021) Properti psikometri Beck Hopelessness Scale pada populasi non-klinis Indonesia. Intuisi: Jurnal Psikologi Ilmiah. 13(1):24–37. https://doi.org/10.15294/intuisi.v13i1.28037

    Article  Google Scholar 

  38. Sukma YN, Puspitasari DN (2023) How is the relationship between hopelessness and suicidal ideation in adolescents? Psychology Research on Education and Social Sciences 4(1):21–27

    Google Scholar 

  39. Anand P, Bhurji N, Williams N, Desai N (2021) Comparison of PHQ-9 and PHQ-2 as Screening Tools for Depression and School Related Stress in Inner City Adolescents. J Prim Care Community Health 12:21501327211053750. https://doi.org/10.1177/21501327211053750

    Article  PubMed  PubMed Central  Google Scholar 

  40. Dian CN, Effendy E, Amin MM (2022) The validation of Indonesian version of Patient Health Questionnaire-9. Open Access Maced J Med Sci 10(T7):193–198. https://doi.org/10.3889/oamjms.2022.9293

    Article  Google Scholar 

  41. Jannah K, Hastuti D, Riany Y (2022) Parenting style and depression among students: The mediating role of self-esteem. Psikohumaniora: Jurnal Penelitian Psikologi 7(1):39–50. https://doi.org/10.21580/pjpp.v7i1.9885

    Article  Google Scholar 

  42. Nartova-Bochaver S, Korneev A, Bochaver K (2021) Validation of the 10-Item Connor-Davidson Resilience Scale: The Case of Russian Youth. Front Psychiatry 10(12):611026. https://doi.org/10.3389/fpsyt.2021.611026

    Article  Google Scholar 

  43. Jørgensen IE, Seedat S (2008) Factor structure of the Connor-Davidson resilience scale in South African adolescents. Int J Adolesc Med Health 20(1):23–32

    Article  PubMed  Google Scholar 

  44. Okuyama J, Funakoshi S, Tomita H, Yamaguchi T, Matsuoka H (2018) Longitudinal characteristics of resilience among adolescents: a high school student cohort study to assess the psychological impact of the Great East Japan Earthquake. Psychiatry Clin Neurosci 72(11):821–835. https://doi.org/10.1111/pcn.12772

    Article  PubMed  Google Scholar 

  45. Gina F, Fitriani Y (2022) Validasi 10-item Connor-Davidson Resilience Scale (10-ITEM CD-RISC) pada ibu bekerja. e-Jurnal Mitra Pendidikan. 6(1):49–57

    Google Scholar 

  46. Mujahidah E, Listiyandini RA (2018) The Influence of Resilience and Empathy toward Depression of Adolescents. Jurnal Psikologi 14(1):60–75

    Article  Google Scholar 

  47. Bello I, Rodríguez-Quiroga A, Quintero J (2023) Suicidal and self-harm behavior in adolescents, an unsolved problem A comprehensive review. Actas Esp Psiquiatr 51(1):10–20

    PubMed  Google Scholar 

  48. Adjorlolo S, Anum A, Amin JM (2022) Validation of the Suicidal Behaviors Questionnaire-Revised in adolescents in Ghana. J Ment Health 31(3):302–308. https://doi.org/10.1080/09638237.2020.1739239

    Article  PubMed  Google Scholar 

  49. Idham AF, Sumantri MA, Rahayu P (2020) Ide dan upaya bunuh diri pada mahasiswa. Intuisi: Jurnal Psikologi Ilmiah 11(3):177–183

    Google Scholar 

  50. Yosep I, Purnama H, Lindayani L, Chen YC, Sudrajat DA, Firdaus MR (2024) The Relationship Between Bullying and Risk of Suicide Among Adolescents During the COVID-19 Pandemic in Indonesia. Soa Chongsonyon Chongsin Uihak 35(1):75–81. https://doi.org/10.5765/jkacap.230012

    Article  PubMed  PubMed Central  Google Scholar 

  51. McKinnon B, Gariépy G, Sentenac M, Elgar FJ (2016) Comportements suicidaires des adolescents dans 32 pays à revenu faible et intermédiaire. Bull World Health Organ 94:340–350. https://doi.org/10.2471/BLT.15.163295

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ibrahim N, Amit N, Che Din N, Ong HC (2017) Gender differences and psychological factors associated with suicidal ideation among youth in Malaysia. Psychol Res Behav Manag 28:129–135. https://doi.org/10.2147/prbm.s125176

    Article  Google Scholar 

  53. Owens SA, Eisenlohr-Moul TA, Prinstein MJ (2020) Understanding when and why some adolescent girls attempt suicide: an emerging framework integrating menstrual cycle fluctuations in risk. Child Dev Perspect 14(2):116–123. https://doi.org/10.1111/cdep.12367

    Article  PubMed  PubMed Central  Google Scholar 

  54. Dean-Boucher A, Robillard CL, Turner BJ (2020) Chronic medical conditions and suicidal behaviors in a nationally representative sample of American adolescents. Soc Psychiatry Psychiatr Epidemiol 55:329–337. https://doi.org/10.1007/s00127-019-01770-2

    Article  PubMed  Google Scholar 

  55. Ferro MA, Rhodes AE, Kimber M, Duncan L, Boyle MH, Georgiades K, Gonzalez A, MacMillan HL (2017) Suicidal behaviour among adolescents and young adults with self-reported chronic illness. Can J Psychiatry 62(12):845–853. https://doi.org/10.1177/0706743717727242

    Article  PubMed  PubMed Central  Google Scholar 

  56. Greydanus D, Patel D, Pratt H (2010) Suicide risk in adolescents with chronic illness: implications for primary care and specialty pediatric practice: a review. Dev Med Child Neurol 52(12):1083–1087. https://doi.org/10.1111/j.1469-8749.2010.03771.x

    Article  PubMed  Google Scholar 

  57. Karasouli E, Latchford G, Owens D (2014) The impact of chronic illness in suicidality: a qualitative exploration. Health Psychol Behav Med 2(1):899–908. https://doi.org/10.1080/21642850.2014.940954

    Article  PubMed  Google Scholar 

  58. Soto-Sanz V, Antonio Piqueras J, Rodriguez-Marin J, Teresa Perez-Vazquez M, Rodríguez-Jiménez T, Castellvi P, Miranda-Mendizábal A, Parés-Badell O, Almenara J, Blasco MJ, Cebria A (2019) Self-esteem and suicidal behaviour in youth: A meta-analysis of longitudinal studies. Psicothema 31(3):246–254. https://doi.org/10.7334/psicothema2018.339

    Article  PubMed  Google Scholar 

  59. Sharaf AY, Thompson EA, Walsh E (2009) Protective effects of self-esteem and family support on suicide risk behaviors among at-risk adolescents. J Child Adolesc Psychiatr Nurs 22(3):160–168. https://doi.org/10.1111/j.1744-6171.2009.00194.x

    Article  PubMed  Google Scholar 

  60. Macalli M, Kinouani S, Texier N, Schück S, Tzourio C (2022) Contribution of perceived loneliness to suicidal thoughts among French university students during the COVID-19 pandemic. Sci Rep 12(1):16833. https://doi.org/10.1038/s41598-022-21288-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Ruiz-Robledillo N, Ferrer-Cascales R, Albaladejo-Blázquez N, Sánchez-SanSegundo M (2019) Family and school contexts as predictors of suicidal behavior among adolescents: The role of depression and anxiety. J Clin Med 8(12):2066. https://doi.org/10.3390/jcm8122066

    Article  PubMed  PubMed Central  Google Scholar 

  62. Saffer BY, Glenn CR, David KE (2015) Clarifying the relationship of parental bonding to suicide ideation and attempts. Suicide Life Threat Behav 45(4):518–528. https://doi.org/10.1111/sltb.12146

    Article  PubMed  Google Scholar 

  63. Kerr DC, Preuss LJ, King CA (2006) Suicidal adolescents’ social support from family and peers: gender-specific associations with psychopathology. J Abnorm Child Psychol 34:99–110. https://doi.org/10.1007/s10802-005-9005-8

    Article  Google Scholar 

  64. Assari S, Lankarani MM (2016) Depressive symptoms are associated with more hopelessness among white than black older adults. Front Public Health 4(4):82. https://doi.org/10.3389/fpubh.2016.00082

    Article  PubMed  PubMed Central  Google Scholar 

  65. Czyz EK, King CA (2015) Longitudinal trajectories of suicidal ideation and subsequent suicide attempts among adolescent inpatients. J Clin Child Adolesc Psychol 44(1):181–193. https://doi.org/10.1080/15374416.2013.836454

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the headmasters of selected high schools for permission to conduct this research. We also extend our thanks to all students who participated in this study.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: DK, ASF; Methodology: DK, ASF, WAWS; Data Collection: DK, ASF, BADS, IF, GS, RFK, ANW, WAWS, ZP, EBW; Data Curation: IF, GS, ANW; Formal Analysis: BADS, RFK, EBW; Writing and Editing The Manuscript: DK, ASF.

Corresponding author

Correspondence to Akbar Satria Fitriawan.

Ethics declarations

Ethics approval and consent to participate

Ethical clearance for this study was obtained from the Institutional Review Board of Universitas Respati Yogyakarta, Indonesia with approval code: 005.3/FIKES/PL/I/2023 on 27 January 2023. Before the data collection, detailed research information was provided to the participants and the informed consent was obtained from them before commencing the study.

Consent for publication

Not applicable.

Competing interests

All authors have no potential conflict of interest disclosed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kurniawan, D., Fitriawan, A.S., Susanti, B.A.D. et al. Predictors of suicidal behaviors among school-going adolescents: a cross sectional study in Indonesia. Middle East Curr Psychiatry 31, 39 (2024). https://doi.org/10.1186/s43045-024-00429-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43045-024-00429-2

Keywords