Suicidal Behavior in University Students in Spain: a Network Analysis

Introduction

Suicidal behavior is a significant mental health concern among university students, yet it remains underexplored through a network approach. Traditional methods often overlook the complex interplay of psychological factors influencing suicidal behavior. This study addresses this gap by (a) examining suicidal behavior using network analysis and (b) identifying the psychological network of suicidal behavior along with protective and risk factors among university students.

Method

The sample consisted of a total of 1048 Spanish university students (M = 20.5 years; SD = 2.5; 58.2% women) from various universities. Several instruments were used to measure suicidal behavior, emotional and behavioral difficulties, prosocial behavior, subjective well-being, emotional intelligence, self-esteem, empathy, affect (both positive and negative), and emotional regulation. Data were analyzed using network analysis to understand the relationships among these variables.

Results

A total of 21.5% of the participants had experienced suicidal thoughts, 26.3% had wished to be dead or not wake up, 15% had considered specific suicide methods or made plans, and 5.8% had attempted suicide at least once. In addition, 18% had engaged in non-suicidal self-injury (NSSI) at least once. Network analysis revealed that suicidal behavior was highly interconnected with other psychological factors, with “considered taking own life” as the most influential node. Key factors included depressive symptoms, positive affect, and emotional intelligence. Suicidal behavior showed a positive connection with depressive symptoms and negative affect and a negative connection with self-esteem and positive affect. Stability analysis confirmed the accuracy of the network estimations, indicating reliable insights into the psychological interconnections.

Conclusions

These findings indicate that suicidal behavior in university students is a complex, dynamic system shaped by cognitive, emotional, and affective factors. Network analysis, through advanced psychopathological and psychometric models, offers valuable insights into suicidal behavior, improving risk and protective factor assessment. This highlights the need for targeted and comprehensive prevention strategies in educational settings.