Development and temporal validation of a screening-based prediction model for suicidal ideation at first contact with university counseling services
- 1Health Care Center, Kitasato University, 1-15-1 Kitazato, Minami-Ku, Sagamihara, Kanagawa 252-0373 Japan
- 2Faculty of Education, Soka University, 1-236 Tangi-Machi, Hachioji, Tokyo 192-8577 Japan
Abstract
Background: Suicidal ideation among university students is a major public health concern. Yet counseling services still lack practical tools that can identify students at elevated risk during their first clinical contact. This study aimed to develop and temporally validate a screening‑based prediction model for suicidal ideation at the time of initial presentation to university counseling services.
Methods: This retrospective observational study analyzed routinely collected data from a Japanese university counseling services. Predictor variables were exclusively derived from annual mental health screening data collected before participants used counseling services. The variables included psychological distress, self‑esteem, lifestyle‑related factors, and demographic characteristics. The outcome was suicidal ideation assessed during the first counseling intake interview. We developed a multivariable logistic regression model using a development cohort of students who accessed counseling services between 2017 and 2019 and temporally validated the model in an independent cohort from 2021 to 2023. We evaluated the model performance using discrimination, measured by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). A reference value threshold was identified using a Youden Index–based cutoff.
Results: The development and validation cohorts included 1,136 and 1,286 students, respectively. The prevalence of suicidal ideation at intake was comparable across cohorts (14.0% and 14.7%). The model achieved moderate discrimination in the development cohort (AUC = 0.71) and good discrimination in the validation cohort (AUC = 0.74), with acceptable calibration in both cohorts. Higher levels of screening‑based psychological distress, particularly feelings of worthlessness, female sex, and lower self-esteem increased the odds of suicidal ideation at intake. Conversely, call-based or early outreach referral routes were associated with lower odds. DCA showed that the model provided greater net benefit than strategies that intervened in all or no students across clinically plausible threshold probabilities, including the Youden Index‑based cutoff (approximately 0.13).
Conclusions: Routinely collected mental health screening data can predict suicidal ideation at the time of initial counseling contact with reasonable accuracy and potential clinical utility. Screening‑based prediction models may strengthen existing student support systems by enabling earlier identification of elevated risk and guiding proportionate intervention strategies.
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BibTeX
@article{yamada2026development,
title = {{Development and temporal validation of a screening-based prediction model for suicidal ideation at first contact with university counseling services}},
author = {Yuko Yamada and Masao Ishitsuka and Tomohisa Ohmachi and Kiun Kato and Togo Aoyama},
journal = {BMC Psychology},
year = {2026},
month = jul,
doi = {10.1186/s40359-026-05207-9},
url = {https://doi.org/10.1186/s40359-026-05207-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development and temporal validation of a screening-based prediction model for suicidal ideation at first contact with university counseling services
AU - Yuko Yamada
AU - Masao Ishitsuka
AU - Tomohisa Ohmachi
AU - Kiun Kato
AU - Togo Aoyama
PY - 2026
DA - 2026/07/25
JO - BMC Psychology
DO - 10.1186/s40359-026-05207-9
UR - https://doi.org/10.1186/s40359-026-05207-9
AB - Background: Suicidal ideation among university students is a major public health concern. Yet counseling services still lack practical tools that can identify students at elevated risk during their first clinical contact. This study aimed to develop and temporally validate a screening‑based prediction model for suicidal ideation at the time of initial presentation to university counseling services. Methods: This retrospective observational study analyzed routinely collected data from a Japanese university counseling services. Predictor variables were exclusively derived from annual mental health screening data collected before participants used counseling services. The variables included psychological distress, self‑esteem, lifestyle‑related factors, and demographic characteristics. The outcome was suicidal ideation assessed during the first counseling intake interview. We developed a multivariable logistic regression model using a development cohort of students who accessed counseling services between 2017 and 2019 and temporally validated the model in an independent cohort from 2021 to 2023. We evaluated the model performance using discrimination, measured by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). A reference value threshold was identified using a Youden Index–based cutoff. Results: The development and validation cohorts included 1,136 and 1,286 students, respectively. The prevalence of suicidal ideation at intake was comparable across cohorts (14.0% and 14.7%). The model achieved moderate discrimination in the development cohort (AUC = 0.71) and good discrimination in the validation cohort (AUC = 0.74), with acceptable calibration in both cohorts. Higher levels of screening‑based psychological distress, particularly feelings of worthlessness, female sex, and lower self-esteem increased the odds of suicidal ideation at intake. Conversely, call-based or early outreach referral routes were associated with lower odds. DCA showed that the model provided greater net benefit than strategies that intervened in all or no students across clinically plausible threshold probabilities, including the Youden Index‑based cutoff (approximately 0.13). Conclusions: Routinely collected mental health screening data can predict suicidal ideation at the time of initial counseling contact with reasonable accuracy and potential clinical utility. Screening‑based prediction models may strengthen existing student support systems by enabling earlier identification of elevated risk and guiding proportionate intervention strategies.
SN - 2050-7283
ER -