Kwon, Hoin
(Department of Counseling Psychology, Jeonju University)
,
Hong, Hyun Ju
(Suicide and School Mental Health Institute, Hallym University)
,
Kweon, Yong-Sil
(Suicide and School Mental Health Institute, Hallym University)
Exploring the risk factors of adolescent suicide is important for effective suicide prevention. This study explored the clustering of adolescent suicides based on six risk factors: mental disorder, broken family, depression, anxiety, previous suicide attempts, and deviant behaviors. Using 173 studen...
Exploring the risk factors of adolescent suicide is important for effective suicide prevention. This study explored the clustering of adolescent suicides based on six risk factors: mental disorder, broken family, depression, anxiety, previous suicide attempts, and deviant behaviors. Using 173 student suicide reports obtained from the Ministry of Education, we evaluated the associations between suicide and variables related to mental disorders; dysfunctional family life; depression and anxiety; previous suicide attempts; deviant behaviors such as drinking and smoking; and school life characteristics, including attendance and discipline, problems within the past year, and incidents prior to suicide. In addition, reports of warning signs just before suicide were included in the analysis. The two-stage cluster analysis classified the students into three clusters: the silent type (cluster 1; 48.55%), in which no risk factors were observed; environmental-risk type (cluster 2: 24.28%), which featured a high frequency of broken households, deviant behaviors such as smoking/drinking and running away from home; and depressive type (cluster 3: 27.17%), which featured a high frequency of mental health problems such as depression, anxiety, and suicide attempts. Identifying the sub-types of adolescent suicide may help to inform tailored suicide prevention and intervention strategies in school.
Exploring the risk factors of adolescent suicide is important for effective suicide prevention. This study explored the clustering of adolescent suicides based on six risk factors: mental disorder, broken family, depression, anxiety, previous suicide attempts, and deviant behaviors. Using 173 student suicide reports obtained from the Ministry of Education, we evaluated the associations between suicide and variables related to mental disorders; dysfunctional family life; depression and anxiety; previous suicide attempts; deviant behaviors such as drinking and smoking; and school life characteristics, including attendance and discipline, problems within the past year, and incidents prior to suicide. In addition, reports of warning signs just before suicide were included in the analysis. The two-stage cluster analysis classified the students into three clusters: the silent type (cluster 1; 48.55%), in which no risk factors were observed; environmental-risk type (cluster 2: 24.28%), which featured a high frequency of broken households, deviant behaviors such as smoking/drinking and running away from home; and depressive type (cluster 3: 27.17%), which featured a high frequency of mental health problems such as depression, anxiety, and suicide attempts. Identifying the sub-types of adolescent suicide may help to inform tailored suicide prevention and intervention strategies in school.
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문제 정의
This study aims to analyze the features of suicide among Korean students based on the 2018 and 2019 data reported in the student suicide reports. In particular, we aim to examine how student suicide can be classified via cluster analysis based on the six previously identified risk factors for adolescent suicide: mental disorder, previous suicide attempts, depression, anxiety, family breakup, and deviant behaviors.
This study explored how student suicides can be clustered based on the six known risk factors for suicide. Cluster analysis identified three categories: the silent type, which does not present any risk factors; the environmental-risk type, in broken families and deviant behaviors such as smoking/drinking, and running away from home are more frequent; and the depressive type, which is characterized by depression and a history of mental disorder.
가설 설정
5% in cluster 2, and 24.4% in cluster 3. The three clusters differed significantly in school belongingness and relationships with teachers. Scheffe’s test confirmed that clusters 1 and 2 had a higher sense of school belongingness than did cluster 3, with no significant difference between clusters 1 and 2.
제안 방법
” Three emotional signs, such as “changes in emotional state” and “avoid social contact,” were included. After rating these three types independently, the frequency of the items within each cluster type was computed, and the total scores for the three types of signs (verbal, behavioral, and emotional) were summed.
A two-step cluster analysis was performed to analyze the types of suicide among adolescents. Based on previous studies, the six risk factors of adolescent suicide, namely family breakup, past suicide attempts, mental disorders, depression, anxiety, and deviant behaviors were included in the cluster analysis. First, the most appropriate number of clusters without hurting cluster quality was determined by considering the Akaike information criterion (AIC), Bayesian information criterion (BIC), and distance measures.
3%). Family breakups, past suicide attempts, psychiatric diagnosis, depression, anxiety, and deviant behaviors were analyzed using frequency analysis. Of the participants, 19.
Based on previous studies, the six risk factors of adolescent suicide, namely family breakup, past suicide attempts, mental disorders, depression, anxiety, and deviant behaviors were included in the cluster analysis. First, the most appropriate number of clusters without hurting cluster quality was determined by considering the Akaike information criterion (AIC), Bayesian information criterion (BIC), and distance measures. This calculation helped to inform the hierarchical cluster analysis based on this was performed.
First, the participants’ demographic and clinical features were analyzed using descriptive statistics.
0%). For a more detailed analysis, we performed a cluster analysis for each of the stressful events (conflict with parents, conflict with friends, dating and relationship problems, conflict with siblings, career- and grade-related events, and problem behaviors). The results confirmed that there were significant differences between the three clusters in dating, relationship problems, and school punishments.
Relationships with teachers were also rated on a 4-point scale from 1 to 4, where higher scores indicate better relationships. Further, whether the school-based mental health screening test was performed and whether the students were classified as normal or in need of management were analyzed.
In this study, a single item that asked about various deviant behaviors in the Student Suicide Report was used: specifically, about smoking, drinking, running away from home, theft, school violence perpetration, and others. The presence of any one of these items was analyzed.
Nevertheless, this study is significant in that it detected various risk factors and pre-suicide signs among adolescents who committed suicide based on teachers’ reports and clustered them into different types.
Second, the subjects were clustered based only on six risk factors–despite a variety of other candidate risk factors such as exposure to childhood trauma or a family history of suicide.
First, the most appropriate number of clusters without hurting cluster quality was determined by considering the Akaike information criterion (AIC), Bayesian information criterion (BIC), and distance measures. This calculation helped to inform the hierarchical cluster analysis based on this was performed. With respect to AIC, BIC, and distance measures, three clusters were determined to be the most appropriate, and cluster quality was good at a silhouette measure of cohesion and separation of 0.
This study explored how student suicides can be clustered into the following three types based on six known risk factors for suicide: the silent type, which does not present any of the risk factors; the environmental-risk type, in which broken families and deviant behaviors such as smoking/drinking and running away from home are more common; and the depressive type, which is characterized by depression and the diagnosis of a mental disorder.
대상 데이터
A total of 258 cases of student suicides were reported from January 2017 to December 2018. After excluding 75 cases due to their missing data required for cluster analysis (i.e., namely status of mental disorder, family breakup, suicide attempts, deviant behaviors, depression, and anxiety), the data from the remaining 173 cases were included in the analysis.
Our analysis included the data provided by 173 student suicide reports. The mean age of the students was 16.
This study used data from student suicide reports submitted to the Suicide and School Mental Health Institute in 2018 and 2019 [14,15]. Two teachers, including the homeroom teacher, wrote the suicide report.
This study used data from student suicide reports submitted to the Suicide and School Mental Health Institute in 2018 and 2019 [14,15]. Two teachers, including the homeroom teacher, wrote the suicide report. A total of 258 cases of student suicides were reported from January 2017 to December 2018.
데이터처리
To examine how the adolescents’ risk factors could be classified, a two-step cluster analysis was performed that considered psychiatric disorders, family breakup, past suicide attempts, depression, and anxiety. The differences in demographic features, clinical and suicide-related features, and school and behaviors between the groups were analyzed with a cross tabulation analysis and oneway analysis of variance (ANOVA) followed by the Scheffe post-hoc test. All statistical analyses were performed using SPSS 25.
The differences in strengths and difficulties among the three clusters were analyzed using one-way ANOVA. There were no significant differences in the social orientation (strength), hyperactivity, or behavioral problems subscales (difficulties) between the three clusters.
The differences in the demographic data between the three clusters were analyzed using one-way ANOVA and Pearson’s chi-squared test.
이론/모형
In this study, the children’s and adolescents’ mental health problems were screened using the Strengths and Difficulties Questionnaire developed by Goodman and standardized into Korean by Ahn et al.
성능/효과
Scheffe’s test confirmed that clusters 1 and 2 had a higher sense of school belongingness than did cluster 3, with no significant difference between clusters 1 and 2. Cluster 1 had better relationships with teachers than did cluster 3, with no significant differences between clusters 1 and 2 and between clusters 2 and 3. There were no significant differences between the clusters in terms of the distribution of school grades. Table 3 shows the school life-related characteristics of the three clusters.
Family breakups, past suicide attempts, psychiatric diagnosis, depression, anxiety, and deviant behaviors were analyzed using frequency analysis. Of the participants, 19.1% were diagnosed with a psychiatric disorder, 23.7% were in a broken family, 12.7% had deviant behaviors, 5.8% had engaged in previous suicide attempts, 26.6% had depression, and 11.6% had anxiety. The most common psychiatric diagnosis among the 33 students with a psychiatric condition was depression (57.
The results showed that the three clusters differed significantly in personal, family, and friend problems but not in problems related to addiction or studies. The analysis of residual structures using Pearson residuals revealed that cluster 2 and cluster 3 had significantly higher rate of personal and friend problems than cluster 1, while cluster 2 had a higher rate of family problems than did the other clusters. Further, the results of the cluster analysis of the presence of non-suicidal self-injuries were significant.
The analysis of residual structures using Pearson residuals revealed that the three clusters were significantly different in terms of socioeconomic status: cluster 1 had a significantly lower percentage of students with a “low” socioeconomic status than did the other groups, while cluster 2 had a significantly higher percentage of students rated to have a “low” socioeconomic status.
참고문헌 (27)
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