The purpose of this study is to increase the use of evidence-based practice, specifically data collection, among paraprofessionals working one-to-one with children with autism. We propose to develop and test strategies to increase their self-efficacy, supervision, and sense of belonging to a professional community, with opportunities for peer comparison and supervisor acknowledgement as mechanisms to increase the use of data collection. We chose data collection because: 1) it is a component of every evidence-based intervention for children with autism and is common to many mental health interventions for other children as well; 2) this foundational practice is essential to monitoring outcomes associated with other practices, and has been associated with more positive outcomes in and of itself; and 3) it results in a measureable work product that lends itself to objectively estimating the effectiveness of our implementation strategies. Our implementation strategy is based on psychological principles that inform behavioral economics. We will use a participatory design approach to build a digital app on the Way To Health platform that allows paraprofessionals to 1) easily collect and upload data; 2) observe how they compare with their peers in data collection; and 3) receive positive recognition from their supervisors and employers in response to frequent and accurate data collection. Specifically, we will: 1) Observe and query 10 paraprofessionals in the field to examine how they collect data, functional and structural barriers to data collection, and paraprofessionals' attitudes, subjective norms and self- efficacy regarding data collection; 2) Using rapid prototyping, integrate Aim 1 findings into the Way To Health app, making data collection easier and more socially desirable, and refine the app based on our observations and data collection; 3) Test the refined app with 20 paraprofessionals from two nave agencies; and 4) Explore how use of other evidence-based autism interventions could be objectively and inexpensively measured and rewarded using the same technology. This project builds on our 30-year-old partnership with Philadelphia's Department of Behavioral Health and our decade of experience studying evidence-based autism interventions in the community. Successful completion of the proposed study will lead to an R01-funded randomized trial to rigorously examine the power of behavioral economics principles, operationalized through digital technology, to improve use of evidence-based mental health interventions in the community. The project directly ties in with our Methods Core in that we will test an implementation strategy development process (rapid cycle prototyping) and refine measures of psychological factors associated with implementation (normative pressure). Given that paraprofessionals are the fastest growing group of health care workers, with frontline responsibility for the care of individuals with a variety of psychiatric and developmental disabilities, the proposed project has broad public health implications with regard to how we improve mental health care.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
1P50MH113840-01
Application #
9376074
Study Section
Special Emphasis Panel (ZMH1)
Project Start
Project End
Budget Start
2017-07-01
Budget End
2018-04-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Type
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Williams, Nathaniel J; Beidas, Rinad S (2018) Annual Research Review: The state of implementation science in child psychology and psychiatry: a review and suggestions to advance the field. J Child Psychol Psychiatry :
Lane-Fall, Meghan B; Cobb, Benjamin T; Cené, Crystal Wiley et al. (2018) Implementation Science in Perioperative Care. Anesthesiol Clin 36:1-15
Hong, Chuan; Ning, Yang; Wang, Shuang et al. (2017) PLEMT: A NOVEL PSEUDOLIKELIHOOD BASED EM TEST FOR HOMOGENEITY IN GENERALIZED EXPONENTIAL TILT MIXTURE MODELS. J Am Stat Assoc 112:1393-1404