Research experience and career factors in relation to mental health problems: Prevalence, risk factors, and machine learning-based predictive estimates.

Background: Mental health issues, including depression, anxiety, and insomnia, are increasingly prevalent among university students and graduates, especially those involved in academic research. The impact of research-related characteristics on mental health remains underexplored.

Aim: We examined this relationship using machine learning alongside traditional statistical analyses and GIS mapping.

Methods: Data from 508 university students and graduates were collected, and encompassed socio-demographics, academic information, research related information, and mental health outcomes. Statistical analyses were performed using SPSS, while spatial analysis was conducted using QGIS and machine learning models were developed with Python with Google Colab.

Results: High prevalence rates of depression (39.8%), anxiety (29.3%), and insomnia (12.2%) emerged. Feature selection highlighted research experience (excluding thesis), research courses during the bachelor's program, and interest in a research-related career as significant predictors of mental health outcomes. CatBoost modeling performed best in accuracy and precision of risk prediction of mental health conditions. Support Vector Machine model performed well in predicting depression, while Random Forest showed consistent low log loss, indicating better calibration across mental health issues. GIS mapping revealed no significant regional heterogeneity in mental health outcomes. Research-related factors, such as research experience and academic pressures, significantly impact the mental health of university students and graduates.

Conclusions: Machine learning models may enable institutions to more effectively identify at-risk students and provide personalized support to foster a supportive research environment, ultimately improving both mental health outcomes and academic success.