CLSSS: Large Scale Mental Health Measurement

Join Dr. Robert Gibbons to discuss how innovations in adaptive testing have helped to optimize the measurement of mental health. These innovations provide an alternative to full scale survey administration that has historically been based primarily on subjective judgment and classical test theory. The discussion will address how computerized adaptive tests can be developed using multidimensional extensions of item response theory, estimates of items (e.g., difficulty, discrimination), and individuals (e.g., severity of depression). Computerized adaptive tests efficiently identify suitable item subsets for each individual that allows different individuals to receive different symptom items that are targeted to their specific impairment level. The result is reduction in patient burden, elimination of clinician burden, and maximization of precision of measurement. Using this technology allows for the collection of information from a thousand or more items adaptively in a few minutes and provides high precision mental health measurement for entire populations via the internet. Applications include screening for depression and anxiety in college students, veterans and active military, children in the welfare system, prisoners, and more.

The Center for Research Informatics in conjunction with the Master of Science in Biomedical Informatics launched the Computational Life Sciences Seminar Series (CLSSS) in October 2017.