Depression and anxiety among school adolescent females: A national study from Jordan

AlAzzam, Manar; AL-Sagarat, Ahmad Yahya; Razeq, Nadin M. Abdel; Alsaraireh, Faris A.

doi:10.1177/01430343211017181

Cited by 5 publications

(7 citation statements)

References 42 publications

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“…In this study, the risk features importance rating for anxiety and depression was estimated, which showed that age, bullying behaviors, PTSD, life satisfaction, and anxiety are the 5 most important features in predicting depression symptoms, whereas psychosomatic symptoms, age, bullying behaviors, and depression symptoms are the most important features in predicting anxiety symptoms. The study findings are consistent with other studies that found that the children’s age, academic score, family income, social and family support, school and home violence, and physical activities are significant and important factors in predicting schoolchildren’s depression and anxiety [ 13 , 15 ]. An important contribution of this study is the classification of schoolchildren at high risk to develop anxiety and depression symptoms.…”

Section: Discussionsupporting

confidence: 92%

“…Schoolchildren's mental health problems including depression and anxiety have been studied by many researchers [12][13][14][15][16].…”

Section: Prior Workmentioning

confidence: 99%

“…Research studies have reported that depression and anxiety lead to several negative consequences on children's health development, such as functional impairment and poor cognitive development, social development, and educational and academic performance. It has been found that depression and anxiety symptoms are associated with many risk factors, including poor lifestyle and eating habits, violent behavior, negative social and family support, and socioeconomic factors [13][14][15][16].…”

Section: Prior Workmentioning

confidence: 99%

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Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

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Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.

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Section: Discussionsupporting

confidence: 92%

“…Schoolchildren's mental health problems including depression and anxiety have been studied by many researchers [12][13][14][15][16].…”

Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

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“…Adolescent girls may be more sensitive and perceive their world in a different way from numerous viewpoints than adolescent boys, and these perceptions may be more complex and, in many cases, more negative. This represents girls’ increased sensitivity at this age, but it may also result in a higher risk of behavioral and emotional problems [ 56 ]. The disappearing sex difference or the slightly higher rate of girls’ substance use compared to boys may also reflect this situation.…”

Section: Discussionmentioning

confidence: 99%

Multi-Level Protective Factors of Adolescent Smoking and Drinking

Dudok

Pikó

2023

EJIHPE

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Adolescence is the most critical life stage for experimentation with substance use; however, this is also the most suitable period for strengthening protective factors and thus promoting adult physical and mental health. Since smoking and drinking still appear among the most frequent types of substance abuse in Europe, this study aims to examine the role of potential protective factors at multiple levels for adolescent smoking and drinking: psychological factors at the individual level, aspects of school attachment at the school level, social support variables at the social level, and measures of quality of life at the level of mental health. This cross-sectional survey involved a sample of adolescents (aged 11–18 years, N = 276) in Budapest and villages in its metropolitan area (Hungary). In addition to descriptive statistics, logistic regression analyses were used to detect odds for potential protective factors. There were no sex differences in adolescents’ substance use. Self-control seems to be a universal and most determining protective factor against substance use, while other potential protective factors (self-esteem, resilience, social support from family or significant others, school attachment, and mental well-being) may also contribute to prevention. However, age and friend support acted as risk factors. Findings suggest that a complex approach to prevention should receive consideration.

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“…Internalization psychosocial problems are common in adolescents. 26 Depression influences the development and exacerbation of FGIDs. 27 Involvement of the emotional-motor system induces gastrointestinal hypersensitivity in IBS.…”

Section: Discussionmentioning

confidence: 99%

Functional gastrointestinal disorders in adolescents during online learning

Simanjuntak,

Prawitasari,

Kadim

et al. 2023

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Background The COVID-19 pandemic necessitated the use of online schooling in order to comply with national and local lockdown guidelines. Online learning required students and teachers to adapt to a new method of schooling. The inability of adolescents to adapt to their environment can interfere with their psychosocial condition and become a risk factor for functional gastrointestinal disorders (FGID). Objective To determine the prevalence of FGID in adolescents and evaluate possible risk factors that existed during online learning. Methods This cross-sectional study was done in children aged 12-18 years who participated in online learning during the COVID-19 pandemic. The diagnosis of FGID was based on Rome IV criteria. Psychosocial problems were assessed using the Pediatric Symptom Checklist Questionnaire–17 (PSC-17) which includes internalization, externalization, and attention subscales. Subjects with a PSC-17 mixed subscale total score of >15 were considered as having an increased likelihood of having a behavioral health disorder. Data were collected by online questionnaires via Google Forms. Results Of 1,413 participants, 23% experienced FGID; of these, 32.6% had >2 FGID diagnostic criteria. Upon multivariate analysis, internalization psychosocial problems were the most common risk factors for FGID, followed by mixed subscale psychosocial problems, unstable internet connection, and not understanding of the material. ConclusionThe prevalence of FGID in adolescents in this study is 23%. Environmental and psychosocial conditions are interrelated as risk factors for FGID in adolescents during online learning in the COVID-19 pandemic.

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Depression and anxiety among school adolescent females: A national study from Jordan

Cited by 5 publications

References 42 publications

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Multi-Level Protective Factors of Adolescent Smoking and Drinking

Functional gastrointestinal disorders in adolescents during online learning

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