Suicidal Ideation among Adolescents: A Case of Bullying Victimization and Emotional Intelligence

Kausar, Noreena; Manaf, Afsheen; Shoaib, Muhammad

doi:10.1177/00302228221120123

Cited by 3 publications

(7 citation statements)

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“…Using bullying victimization, emotional intelligence, and suicidal ideation as a perspective, this article attempted to study suicidal ideation in teenagers by N. Kausar et al, [33] In the Gujrat district of the Punjab region to gather information from adolescents, the researchers chose the Teacher's Screening Items, Consent Form, Demo-graphic Form, Illinois Bullying Scale (Espelage & Holt, 2001), Emotional Intelligence Scale for Children and Adolescents (Batool & Hayat, 2018), and Suicidal Ideation Attributes Scale. Software for data analysis, such as SPSS and MS Excel was used.…”

Section: Authorsmentioning

confidence: 99%

Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey

Haroon,

Minhas,

Sabahat

et al. 2023

VFAST trans. softw. eng.

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Mental health is recognized as a non-communicable disease that impairs human lives, sometimes beyond recovery. While everyone is at risk of developing a mental illness, adolescents are more prone to it due to various factors like hormonal changes, study pressure, social pressure, etc. If mental health goes ignored at this stage, it can cause serious, even fatal problems later on in life, which not only impacts a family but also the young workforce of a country. Hence, constant efforts are being made for the early detection of mental disorders so they can be treated better. Early prediction of mental health issues is a classic machine learning problem relying on patient history and data. In this survey, we discuss a total of 22 previous research papers based on machine learning algorithms and other statistical analysis tools employed for the said task and compare their efficacy. The research papers are categorized into different mental health disorders such as 1) Methods for predicting Depression and Anxiety 2) Methods for Suidial Prevalence 3) Methods for Predicting Autism Spectrum Disorder (ASD) 4) Methods for Predicting Substance Abuse among adolescents. On the basis of accuracy, the performance of machine learning prediction models was compared. CNN models, Random Forest, and XGBoost generally performed better than other models. There is centralized research in Pakistan on mental health based on machine learning so SPSS and other tools are mostly used for data analysis. The findings suggest that Machine learning algorithms can be effective for classifying and early predicting high-risk factors among adolescents

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Section: Authorsmentioning

confidence: 99%

Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey

Haroon,

Minhas,

Sabahat

et al. 2023

VFAST trans. softw. eng.

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“…[3] One recent study found that bullying victimization and low emotional intelligence were associated with suicidal ideation among adolescents. [4] This highlights the need for interventions to address bullying victimization and emotional intelligence to prevent suicidal ideation among young people. In addition to the negative effects on mental health, bullying victimization can also negatively affect academic achievement and school attendance.…”

Section: Introductionmentioning

confidence: 99%

Bullying Victimization among in-School Adolescents in Sierra Leone: A cross-sectional Analysis of the 2017 Sierra Leone Global School-Based Health Survey.

Osborne,

James,

Bangura

et al. 2023

Preprint

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Background Adolescent bullying victimization is recognized as a public health and mental health problem in many countries. However, data on bullying victimization's prevalence and risk factors is scarce in sub-Saharan Africa Sierra Leone. This research aimed to determine bullying victimization prevalence and its associated factors among Sierra Leonean school-going adolescents. Methods The Sierra Leone 2017 Global School-based Health Survey (GSHS) dataset was analyzed. The outcome variable was the respondent's self-report of bullying victimization ("How many days in the previous 30 days were you bullied?"). Descriptive, Pearson chi-square and binary logistic regression analyses were conducted. The regression analysis yielded adjusted odds ratios (aOR) with 95% confidence intervals (CIs) and a significance level of p 0.05. Results Bullying victimization was prevalent among 48.7% of the in-school adolescents. Adolescents who drank alcohol [aOR = 2.48, 95% CI = 1.50–4.10], who reported feelings of loneliness [aOR = 1.51, 95% CI = 1.07–2.14] and who had attempted suicide [aOR = 1.72, 95% CI = 1.03–2.87] were also more likely to be bullied. Also, school truancy [aOR = 1.53, 95% CI = 1.24–1.88] among teenagers was associated with an increased risk of being bullied. Conclusions Our findings suggest that bullying is a widespread problem among Sierra Leonean school-aged youth, and alcohol drinking, loneliness, suicide attempt and school truancy are potential risk factors. In light of the aforementioned causes of bullying in schools, policymakers and school administrators in Sierra Leone need to develop and execute anti-bullying policies and initiatives that target the underlying risk factors of bullying among teenagers.

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“…The study also found that socio-cultural, contextual, and socio-economic variations in the sub-region could contribute to bullying victimization. The post-conflict dynamics in Sierra Leone could trigger compulsive behaviours in school-going adolescents who may still have social exclusion through turbulent moments [6].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, to the negative effects on mental health, research has indicated that adolescents who skip school or drop out are more likely to become victims of bullying and this can have long-term consequences for their future opportunities and well-being. [6], Addressing bullying victimization in schools is therefore important for young people's mental health, academic success, and future prospects. Many interventions can be used to address bullying victimization among in-school adolescents.…”

Section: Introductionmentioning

confidence: 99%

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Bullying victimization among in-school adolescents in Sierra Leone: A cross-sectional analysis of the 2017 Sierra Leone Global School-Based Health Survey

Osborne,

James,

Bangura

et al. 2023

PLOS Glob Public Health

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Adolescent bullying victimization is recognized as a public health and mental health problem in many countries. However, data on bullying victimization’s prevalence and risk factors is scarce in sub-Saharan Africa Sierra Leone. This research aimed to determine bullying victimization prevalence and its associated factors among Sierra Leonean school-going adolescents. The Sierra Leone 2017 Global School-based Health Survey (GSHS) dataset was analyzed. The outcome variable was the respondent’s self-report of bullying victimization ("How many days in the previous 30 days were you bullied?"). Descriptive, Pearson chi-square and binary logistic regression analyses were conducted. The regression analysis yielded adjusted odds ratios (aOR) with 95% confidence intervals (CIs) and a significance level of p 0.05. Bullying victimization was prevalent among 48.7% of the in-school adolescents. Adolescents who drank alcohol [aOR = 2.48, 95% CI = 1.50–4.10], who reported feelings of loneliness [aOR = 1.51, 95% CI = 1.07–2.14] and who had attempted suicide [aOR = 1.72, 95% CI = 1.03–2.87] were also more likely to be bullied. Also, school truancy [aOR = 1.53, 95% CI = 1.24–1.88] among teenagers was associated with an increased risk of being bullied. Our findings suggest that bullying is a widespread problem among Sierra Leonean school-aged youth, and alcohol drinking, loneliness, suicide attempt and school truancy are potential risk factors. In light of the aforementioned causes of bullying in schools, policymakers and school administrators in Sierra Leone need to develop and execute anti-bullying policies and initiatives that target the underlying risk factors of bullying among teenagers.

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Suicidal Ideation among Adolescents: A Case of Bullying Victimization and Emotional Intelligence

Cited by 3 publications

References 65 publications

Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey

Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey

Bullying Victimization among in-School Adolescents in Sierra Leone: A cross-sectional Analysis of the 2017 Sierra Leone Global School-Based Health Survey.

Bullying victimization among in-school adolescents in Sierra Leone: A cross-sectional analysis of the 2017 Sierra Leone Global School-Based Health Survey

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