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 t...
| Published in: | International Journal of Social Psychiatry Vol. 72; no. 2; pp. 294 - 311 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
| Format: | Article |
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Sage Publications Inc.
Mar2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191949893&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191949893 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00207640 ISP jtl: International Journal of Social Psychiatry issn: 00207640 maglogo: Y pubinfo: dt: Mar2026 vid: 72 iid: 2 pid: 344 pub: Sage Publications Inc. artinfo: ui: 191949893 10.1177/00207640251358085 ppf: 294 ppct: 17 formats: tig: atl: Research experience and career factors in relation to mental health problems: Prevalence, risk factors, and machine learning-based predictive estimates. aug: au: Mamun, Mohammed A. Huraira, Md. Abu Begum, Momotaj Hasan, MD. Hamed Khan, Md. Maruf Faruk, Md. Omar Kibria, Mohammad Aktar, Sabrina Muntashir, Naoroj Das, Pronab Rahman, Sadikur Siddiky, Aysha Hasan, Md. Mehedi Wazed, Rubiya Das, Milan Kumar Akter, Sharmin Haque, Anonna Ferdaus, Jannatul Hasan, Md Emran ALmerab, Moneerah Mohammad affil: CHINTA Research Bangladesh, Savar, Dhaka, Bangladesh Department of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh Department of Public Health, University of South Asia, Dhaka, Bangladesh Department of Public Administration, Jahangirnagar University, Dhaka, Bangladesh Department of Sociology, Noakhali Science and Technology University, Noakhali, Bangladesh Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh Department of Biochemistry and Molecular Biology, Primeasia University, Dhaka, Bangladesh Saic College of Medical Science and Technology, Dhaka, Bangladesh Department of Sociology, Shahjalal University of Science and Technology, Sylhet, Bangladesh Department of Library and Information Science, National University, Gazipur, Bangladesh Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh One Health Institute, Chattogram Veterinary and Animal Sciences University, Chattogram, Bangladesh Department of Philosophy, Jahangirnagar University, Savar, Dhaka, Bangladesh Department of Food Technology and Nutrition Science, Noakhali Science and Technology University, Noakhali, Bangladesh Department of Anthropology, Rajshahi University, Rajshahi, Bangladesh Department of Food and Nutrition, Govt. College of Applied Human Science, University of Dhaka, Dhaka, Bangladesh School of Science and Technology, Bangladesh Open University, Gazipur, Bangladesh Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Bangabandhu Sheikh Mujib Medical College, Faridpur, Bangladesh Department of Public Health, Hamdard University Bangladesh, Gazaria, Bangladesh School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China Department of Psychology, College of Education and Human Development, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia su: Bangladesh Mental illness risk factors Mental depression risk factors Mental health Anxiety Experience Students College graduates Psychology of college students Social support Psychosocial factors Vocational guidance Mental depression Disease risk factors Psychiatric epidemiology Risk assessment Random forest algorithms Scale analysis (Psychology) Boosting algorithms Research funding Prediction models Cronbach's alpha Data analysis Receiver operating characteristic curves Insomnia Statistical sampling Questionnaires Logistic regression analysis Disease prevalence Descriptive statistics Severity of illness index Chi-squared test Support vector machines Research Geographic information systems Statistics Machine learning Data analysis software Confidence intervals Comparative studies sug: subj: Mental illness risk factors Mental depression risk factors Mental health Anxiety Experience Students College graduates Psychology of college students Social support Psychosocial factors Vocational guidance Mental depression Disease risk factors Bangladesh Offices of Mental Health Practitioners (except Physicians) Marketing Research and Public Opinion Polling Vocational Rehabilitation Services Other Individual and Family Services Psychiatric epidemiology Risk assessment Random forest algorithms Scale analysis (Psychology) Boosting algorithms Research funding Prediction models Cronbach's alpha Data analysis Receiver operating characteristic curves Insomnia Statistical sampling Questionnaires Logistic regression analysis Disease prevalence Descriptive statistics Severity of illness index Chi-squared test Support vector machines Research Geographic information systems Statistics Machine learning Data analysis software Confidence intervals Comparative studies keyword: academic research GIS mapping mental health supervised machine learning thesis students academic research GIS mapping mental health supervised machine learning thesis students ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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