Related Experiment Video
Updated: Jun 14, 2025
12:18
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
7.5K
Reconciling discrepant universal screening data to improve decision-making: A Bayesian logistic regression approach.
Nathaniel von der Embse1, Sonja Winter2, Wes Bonifay2
1Department of Educational and Psychological Studies at the University of South Florida.
Journal of School Psychology
|June 12, 2025
Summary
Integrating student background data improves mental health screening accuracy. This approach helps identify students needing early intervention services more effectively than single-rater methods.
Area of Science:
- Educational Psychology
- Child and Adolescent Mental Health
- Data Science in Education
Background:
- Many students with mental health needs lack timely support.
- Universal screening is key for early intervention, but current methods are limited.
- Multi-informant assessment is best practice but not applied to universal screening.
Purpose of the Study:
- To develop a Bayesian model for universal mental health screening using student background data.
- To validate cut scores derived from background information.
- To assess the added value of teacher and student self-reports.
Main Methods:
- A Bayesian statistical model was used to incorporate student background information (demographics, referrals, risk statuses).
- Background information generated cut scores in a training sample and were validated in a test sample.
- Sensitivity and specificity were analyzed with and without teacher/student self-reports.
Main Results:
- Incorporating background information significantly improved the accurate identification of students at risk for mental health needs.
- The model demonstrated promise in categorizing students into low, medium, and high-risk groups.
- Background data enhanced the precision of risk identification.
Conclusions:
- Student background information is valuable for accurate universal mental health screening.
- This data-driven approach supports timely identification and intervention for students.
- Future research and practice should consider integrating comprehensive student data for mental health support.
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