The Content, Pedagogy, Implementation, and Context Components (CPIC) Study
Client
AMTC & Associates LLC through a grant from the US Department of Health and Human Services (HHS)
Challenge
The Content, Pedagogy, Implementation, and Context Components (CPIC) Study aims to better understand Evidence Based Programs (EBP) for Teen Pregnancy Prevention (TPP). Our challenge is to determine the relationship between the intended and the implemented core components using empirical data, collected over multiple years of APP evaluation work conducted by AMTC & Associates; and, to determine which implemented core components are most essential for producing desired outcomes.
Solution
In Phase I: assemble a cleaned, merged data set comprising 11 years of survey, implementation, and attendance data for TPP programs utilizing 6 curricula. In the upcoming Phase II: propose hypotheses and provide categorical data analysis.
Impact
Overall, this study will aid implementers in adapting program components for target populations, researchers in testing the effects of individual components on participant outcomes, and policymakers and funders in identifying and prioritizing interventions with promising components. Furthermore, this study will provide TPP researchers with multiple examples of how to conduct evaluation studies that focus on the core components of the program implemented.
Methodology
We extracted curriculum information from AMTC’s implementation database (OPTS) to aid in the qualitative analysis of implemented core components. We linked the OPTS database to a database, obtained through local evaluation efforts, that consists of responses to pre- and post-surveys, from youth that participated in the implementation of the six EBPs studied in Phase I. This newly created database will be used to determine the relationship between various core components and the intended outcomes of program participation.
Results
We found sufficient data to analyze five of the six curricula. We detailed multiple versions of each curriculum to help identify core components for delivered classes. We assembled disparate survey data sets measuring outcomes across programs spanning 11 years and matched participants with their corresponding attendance records.
Tools Used: Python and SQL for data extraction, cleaning and pre-processing; R for statistical analysis (tidyverse, knitr, dplyr, psych, caret).
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