Ideação suicida e fatores associados entre estudantes de ensino médio e superior: uma análise hierarquizada

Lima, Cássio de Almeida; Messias, Romerson Brito; Brito, Alexandre Botelho; Ferreira, Thalita Bahia; Barbosa, Mariane Silveira; Pinho, Lucinéia de; Brito, Maria Fernanda Santos Figueiredo; Silveira, Marise Fagundes

doi:10.1590/0047-2085000000342

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…Regarding gender, recent evidence contrasted the view that female gender posits a greater risk of suicidal ideation (Eskin et al, 2011; Rogers and Joiner, 2017). The fact that young adult males are at higher risk of suicidal ideation has lately gained traction (Lima et al, 2021; Yarar et al, 2023). These can be better understood in light of the more extensive use of statistical tools, taking into account multiple variables simultaneously, and thus examining the corrected weights of gender on suicidal ideation (Gui et al, 2022), although cultural differences need to be thoroughly assessed, as they can shift the weights in favour of males or females (Kaggwa et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Frequency and machine learning predictors of depressive, anxiety, obsessive-compulsive symptoms, and suicidal ideation among university students

Meda

Pardini

Rigobello

et al. 2023

Preprint

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Introduction Prospective studies on mental health of students showed that young adults enroled in university are affected by poorer mental health than other working peers or adults, and this condition is responsible for a large proportion of disability-adjusted life-years Methods We enrolled 1388 students at the baseline (and 557 completed follow-up after six months) who reported their demographic information and completed self-report questionnaires on depressive, anxiety and obsessive-compulsive symptoms. We applied multiple regression modelling and supervised machine learning to evince associations and predict the risk factors of poorer mental health at baseline and follow-up Results Approximately one out of five students reported severe depressive symptoms and/or suicidal ideation. An association of economic worry with depression was evidenced at the beginning of the study (when there was a high frequency of worry OR = 3.11 [1.88 - 5.15]) and during followup.Supervised machine learning exhibited high accuracy in predicting the students who maintained well-being (balanced accuracy = 0.85) or absence of suicidal ideation, but its performance was almost null in identifying those whose symptoms worsened. Conclusions Students' severe mental health problems are reaching worrying percentages, and few demographic factors can be leveraged to predict poor mental health outcomes. Further research including people with lived experience is crucial to assess students' needs and improve the prediction of those at risk of developing worse symptoms.

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Section: Discussionmentioning

confidence: 99%

Frequency and machine learning predictors of depressive, anxiety, obsessive-compulsive symptoms, and suicidal ideation among university students

Meda

Pardini

Rigobello

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Regarding gender, recent evidence contrasted the view that female gender posits a greater risk of suicidal ideation (Eskin et al, 2011;Rogers and Joiner, 2017). The fact that young adult males are at higher risk of suicidal ideation has been getting more traction lastly (Lima et al, 2021;Yarar et al, 2023). These can be better understood in light of the more extensive use of statistical tools, taking into account multiple variables simultaneously and thus examining the corrected weights of gender on suicidal ideation (Gui et al, 2022), although cultural differences need to be thoroughly assessed, as they can shift the weights in favour of males or females (Kaggwa et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…The fact that young adult males are at higher risk of suicidal ideation has been getting more traction lastly (Lima et al. , 2021 ; Yarar et al. , 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students

Meda

Pardini

Rigobello

et al. 2023

Epidemiol Psychiatr Sci

View full text Add to dashboard Cite

Aims Prospective studies on the mental health of university students highlighted a major concern. Specifically, young adults in academia are affected by markedly worse mental health status than their peers or adults in other vocations. This situation predisposes to exacerbated disability-adjusted life-years. Methods We enroled 1,388 students at the baseline, 557 of whom completed follow-up after 6 months, incorporating their demographic information and self-report questionnaires on depressive, anxiety and obsessive–compulsive symptoms. We applied multiple regression modelling to determine associations – at baseline – between demographic factors and self-reported mental health measures and supervised machine learning algorithms to predict the risk of poorer mental health at follow-up, by leveraging the demographic and clinical information collected at baseline. Results Approximately one out of five students reported severe depressive symptoms and/or suicidal ideation. An association of economic worry with depression was evidenced both at baseline (when high-frequency worry odds ratio = 3.11 [1.88–5.15]) and during follow-up. The random forest algorithm exhibited high accuracy in predicting the students who maintained well-being (balanced accuracy = 0.85) or absence of suicidal ideation but low accuracy for those whose symptoms worsened (balanced accuracy = 0.49). The most important features used for prediction were the cognitive and somatic symptoms of depression. However, while the negative predictive value of worsened symptoms after 6 months of enrolment was 0.89, the positive predictive value is basically null. Conclusions Students’ severe mental health problems reached worrying levels, and demographic factors were poor predictors of mental health outcomes. Further research including people with lived experience will be crucial to better assess students’ mental health needs and improve the predictive outcome for those most at risk of worsening symptoms.

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Ideação suicida e fatores associados entre estudantes de ensino médio e superior: uma análise hierarquizada

Cited by 2 publications

References 41 publications

Frequency and machine learning predictors of depressive, anxiety, obsessive-compulsive symptoms, and suicidal ideation among university students

Frequency and machine learning predictors of depressive, anxiety, obsessive-compulsive symptoms, and suicidal ideation among university students

Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students

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