Depression and Addiction as Key Predictors of Suicidal Ideation Among Medical and Nursing Students: An Integrated Analysis Using Regression, ROC, and Structural Equation Modeling

AuthorMajid Dastrasen
AuthorMehdi Rezvaniaminen
AuthorAlireza Rejalien
AuthorAmirhossein Enayatien
AuthorFatemeh Mirien
AuthorLeila Mohammadifarden
AuthorSajad Keikhaen
AuthorNazanin Yousefian Miandoaben
AuthorZahra Pournamdaren
AuthorMahmood Rezvaniaminen
AuthorFatemeh Rigien
AuthorHanie Dahmardehen
AuthorHossein Keikhaen
AuthorMaryam Azizien
AuthorAlireza Salaren
AuthorAliakbar Keykhaen
OrcidMajid Dastras [0000-0001-8088-0308]en
OrcidMehdi Rezvaniamin [0009-0008-3886-1478]en
OrcidAlireza Rejali [0009-0008-1280-0159]en
OrcidAmirhossein Enayati [0009-0002-3195-0407]en
OrcidFatemeh Miri [0009-0000-9767-2759]en
OrcidLeila Mohammadifard [0000-0003-4532-5917]en
OrcidSajad Keikha [0009-0005-3696-4076]en
OrcidNazanin Yousefian Miandoab [0000-0002-2614-2918]en
OrcidZahra Pournamdar [0000-0001-9859-0106]en
OrcidMahmood Rezvaniamin [0000-0001-8957-5078]en
OrcidHanie Dahmardeh [0000-0001-6507-5706]en
OrcidAlireza Salar [0000-0002-5280-0283]en
OrcidAliakbar Keykha [0000-0003-2378-0110]en
Issued Date2026-09-30en
AbstractBackground: Suicidal ideation among medical and nursing students is a clinically important concern for student mental health. Recent regional evidence from medical sciences students in Iran indicates that suicidal ideation is associated with mental health problems, while the broader student literature links suicidal thoughts to depression, hopelessness, and behavioral vulnerability. Objectives: This study examined depression, addiction tendency, and selected sociodemographic correlates of suicidal ideation among Iranian medical and nursing undergraduates. Methods: We conducted a cross-sectional study among undergraduate medical and nursing students at Zahedan University of Medical Sciences. Using a two-stage stratified cluster sampling method, we invited 600 eligible students; 20 declined participation and 18 returned incomplete questionnaires, yielding 562 analyzable responses. We assessed suicidal ideation (BSSI), depression (BDI-II), and addiction tendency (ZAPS) using standardized Persian instruments. We estimated associations using multiple regression, evaluated discrimination using ROC/AUC analysis, and examined hypothesized direct and indirect pathways using structural equation modeling (SEM). We also assessed regression assumptions and multicollinearity. Results: Depression showed the strongest cross-sectional association with suicidal ideation, whereas addiction tendency showed a smaller independent association. The multivariable model explained more than half of the variance in suicidal ideation. The logistic model demonstrated high apparent discrimination in the same cross-sectional sample (AUC = 0.92). SEM supported a direct association between depression and suicidal ideation, as well as a statistically significant indirect pathway via addiction tendency (standardized indirect effect = 0.077; 95% bootstrap CI: 0.033 to 0.124). Conclusions: In this single-site cross-sectional sample, depression was the primary correlate of suicidal ideation, whereas addiction tendency provided independent explanatory information and contributed to a statistically significant indirect pathway. Because the study was cross-sectional and the model was not externally validated, the indirect pathway should not be interpreted causally, and the high AUC should be interpreted with caution. Prospective multicenter studies are needed before this model is used to guide screening or implementation decisions.en
DOIhttps://doi.org/10.5812/ijpbs-165445en
URIhttps://brieflands.com/journals/ijpbs/articles/165445en
KeywordAddiction Potentialen
KeywordCross-sectional Studyen
KeywordIranen
KeywordMedical and Nursing Studentsen
KeywordROC/AUCen
KeywordStructural Equation Modeling (SEM)en
KeywordSuicidal Ideationen
KeywordDepressionen
PublisherBrieflandsen
TitleDepression and Addiction as Key Predictors of Suicidal Ideation Among Medical and Nursing Students: An Integrated Analysis Using Regression, ROC, and Structural Equation Modelingen
TypeResearch Articleen

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