Community mental health, psychosis, suicidality
Phalen, P., Grossmann, J., Bruder, T., Jeong, J., Calmes, C., Mcgrath, K., Malouf, E., James, A., Romero, E., and Bennett, M. (2022). Description of a Dialectical Behavior Therapy Program in a Veterans Affairs Health Care System. Evaluation and Program Planning, 92, 102098. pdf
Phalen, P., Millman, Z., Rakhshan Rouhakhtar, P., Andorko, N., Reeves, G., & Schiffman, J. (2021). Categorical Versus Dimensional Models of Early Psychosis. Early Intervention in Psychiatry, 16(1):42-50. pdf
Jones, N., Atterbury, K., Byrne, L., Carras, M., Hansen, M., & Phalen, P. (2021). Lived Experience, Research Leadership, and the Transformation of Mental Health Services: Building a Researcher Pipeline. Psychiatric Services, 72(5), 591-593.
Phalen, P., Bridgeford, E., Gant, L., Kivisto, A., Ray, B., & Fitzgerald, S. (2020). Baltimore Ceasefire 365: Estimated impact of a recurring community-led ceasefire on gun violence. American Journal of Public Health, 110(4), 554-559.
Phalen, P. L., Warman, D., Martin, J., & Lysaker, P. (2018). The stigma of voice-hearing experiences: Religiousness and voice-hearing contents matter. Stigma and Health, 3(1), 77-84. pdf
Phalen, P. L. (2017). Psychological Distress and Rates of Health Insurance Coverage and Use and Affordability of Mental Health Services, 2013–2014. Psychiatric Services, 68(5), 512-515.
Phalen, P. L. (September, 2013), Psychiatrists and African Traditional Healers Ally on Mental Health. Humanosphere.
National Institute of Mental Health (NIMH). Targeting Emotion Dysregulation to Reduce Suicide in People with Psychosis. PI: Peter L. Phalen, PsyD. $721,011.14 (total direct costs). 09/01/22-08/31/26.
VISN 5 VA Capitol Healthcare Network MIRECC. Suicidality among patients with psychosis. PI: Peter L. Phalen, PsyD. $24,089 (total direct costs). 09/01/19-11/30/20
National Institute of Justice. NIJ Recidivism Forecasting Challenge Winner: Team DEAP (award number: 2021-nij-rec-ch-0015). Monetary awards for 1st through 4th place finishes in the NIJ forecasting recidivism challenge, using machine learning techniques to predict blinded outcomes on a real-world dataset. See description of the contest and our final report.