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Reconciling discrepant universal screening data to improve decision-making: A Bayesian logistic regression approach.

Nathaniel von der Embse1, Sonja Winter2, Wes Bonifay2

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Integrating student background data improves mental health screening accuracy. This approach helps identify students needing early intervention services more effectively than single-rater methods.

Keywords:
Bayesian logistic regressionMulti-informant decision-makingUniversal screening

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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.