CEnTR*IMPACT

Overview

The centrimpact R package provides a comprehensive toolkit for analyzing and visualizing community engaged research metrics as highlighted in the CEnTR*IMPACT framework (Price, 2024). It offers specialized functions for researcher-partner alignment, project dynamics, cascade effects, and creating publication-ready visualizations.

Based on the CENTR*IMPACT framework, this package quantifies the following critical concepts in community engaged research:

Alignment: The shared vision between researchers and partners ( $S_a$ ).
Cascade Effects: The ripple effect of work from direct participants to the third degree of separation ( $S_c$ ).
Dynamics: Ways in which the project is carried out modeled on the CBPR framework ( $S_d$ ).
Indicators: “Traditional” markers of academic productivity, such as individuals served, number of outputs, etc.

Installation

You can install this review version of centrimpact from GitHub with:

# Install devtools if not already installed
if (!require("devtools")) install.packages("devtools")

# Install centrimpact
devtools::install_github("CENTR-IMPACT/centrimpact-review")

Usage

Project Alignment Score

The Alignment Score ( $S_a$ ) quantifies the degree of consensus between researchers and partners across key project areas such as Goals, Values, Roles, and Resources.

The Project Alignment can be visualized either with a Slopegraph or an Abacus Plot.

library(centrimpact)

# Generate example data
alignment_data <- generate_alignment_data()

# Analyze alignment data
alignment_results <- analyze_alignment(alignment_data)

# Visualize results with a Slopegraph
plot_slopegraph <- visualize_alignment(alignment_results)

# Visualize results with an Abacus Plot
plot_abacus <- visualize_abacus(alignment_results)

Cascade Effects Score

The Cascade Effects Score ( $S_c$ ) quantifies the degree to which information and power is distributed from the core participants across three degrees. This analysis is based on social network analysis principles.

The Cascade Effects can be visualized as a radial bar chart or “racetrack plot.”

library(centrimpact)

# Generate example data
cascade_data <- generate_cascade_data()

# Analyze alignment data
cascade_results <- analyze_cascade(cascade_data)

# Visualize results
plot_cascade <- visualize_cascade(cascade_results)

Project Dynamics Score

The Project Dynamics Score ( $S_d$ ) quantifies the degree to which the various aspects of a community engaged research project as outlined by the Community Based Participatory Research framework (Wallerstein & Duran, 2010; Wallerstein et al., 2020).

The Project Dynamics can be visualized as a rose chart.

library(centrimpact)

# Generate example data
dynamics_data <- generate_dynamics_data()

# Analyze alignment data
dynamics_results <- analyze_dynamics(dynamics_data)

# Visualize results
plot_dynamics <- visualize_dynamics(dynamics_results)

Project Indicators

The Project Indicators are more traditional academic indicators such as individuals served, outputs generated, outcomes met, etc. These provide the typical “how many” metrics often reported in faculty reviews and final reports.

The Project Indicators can be visualized as a horizontal bubble chart. No separate analysis function is needed as these are direct counts.

library(centrimpact)

# Generate example data
indicators_data <- generate_indicators_data()

# Visualize results
plot_indicators <- visualize_indicators(indicators_data)

Styling Plots

All visualizations created with centrimpact return ggplot2 objects. This allows for further customization (colors, fonts, etc.) using standard ggplot2 functions.

Citing the CEnTR*IMPACT Framework

If you wish to cite the CEnTR*IMPACT framework in your research, please use the following reference:

Price, J. F. (2024). CEnTR*IMPACT: Community Engaged and Transformative Research – Inclusive Measurement of Projects & Community Transformation (CUMU-Collaboratory Fellowship Report). Coalition of Urban and Metropolitan Universities. https://cumuonline.org/wp-content/uploads/2024-CUMU-Collaboratory-Fellowship-Report.pdf

Acknowledgments

This work was supported in part by the Coalition of Urban and Metropolitan Universities (CUMU) and The Collaboratory Research Fellowship Program.

References

Wallerstein, N., & Duran, B. (2010). Community-Based Participatory Research Contributions to Intervention Research: The Intersection of Science and Practice to Improve Health Equity. American Journal of Public Health, 100(S1), S40–S46. 10.2105/AJPH.2009.184036

Wallerstein, N., et al. (2020). Engage for Equity: A Long-Term Study of Community-Based Participatory Research and Community-Engaged Research Practices and Outcomes. Health Education & Behavior, 47(3), 380–390. 10.1177/1090198119897075