Using Concept Mapping to Identify Community Partners’ and Researchers’ Perceptions of Social Justice: A Path Toward Eliminating Chronic Disease Disparities
PMCID: PMC11249129
PMID: 39011082
Abstract
Background: A social justice framework can be used to inform healthy equity-focused research, and operationalizing social justice can inform strategic planning for research and practice models. This study aimed to develop a working definition of social justice based on input from a diverse group of collaborators to better inform the work conducted within the Center for Research, Health, and Social Justice. Methods: A concept mapping study was conducted from March to May 2022. A prompt designed to elicit social justice themes was developed (phase 1). At a study website, participants brainstormed statements that represented their definition of social justice (phase 2). Participants then sorted statements based on similarity and rated statements on importance (phase 3). Multidimensional scaling and hierarchical cluster analysis were used to identify nonoverlapping thematic clusters of statements (phase 4). Models were reviewed for best fit, and clusters were assigned names based on theme (phase 5). Results: Participants ( n = 49) generated 52 unique statements that were sorted into 5 clusters describing social justice themes. Clusters included (1) Empathy, Awareness, and Understanding ( n = 11); (2) Education and Systems Change ( n = 10); (3) Policy Design and Implementation ( n = 9); (4) Equity and Leveling the Playing Field ( n = 11); and (5) Access to Services and Fair Living Standard ( n = 11). High mean cluster ratings ranging from 5.22 to 6.02 out of 7 indicated all clusters were rated as being very important aspects of social justice. Conclusions: These data can guide the restructuring of research ecosystems that help eliminate race- and place-based health disparities.
Full Text
After participants completed the sorting and rating tasks, participant sorting data were analyzed using nonmetric multidimensional scaling. Using an algorithm, each statement was assigned a coordinate (x,y) in two-dimensional space with each statement being represented by a point on a “point map” (see Fig. 1). Points on the map that were closer to each other represented statements that were sorted together more often by participants. The stress value of the model (0.27), which indicates the fit of the multidimensional scaling analysis, fell within the range of stress values reported in previous research, indicating good fit and congruence between the processed data and the raw data.
Cluster map of social justice themes. Numbered points on the map correspond to numbered statements displayed in Table 2. Greater number of layers represent clusters with higher mean statement ratings.
Table 1 displays characteristics of participants in the sample who completed the brainstorming exercise (n = 49). The average age of participants was 52.2 (standard deviation [SD] = 14.2) and approximately two-thirds (67.4%) identified as women. The majority identified as either Black or African American (51.0%) and White or European American (40.8%). Multiple professions were represented including academic researchers (34.7%), community advocates (12.2%), community organization administrators (8.2%), public health practitioners (6.1%), and community health workers (6.1%). Participants reported an average of 18.7 years (SD = 13.0) in their professions.
Mean ratings are based on responses to the prompt, “This is something that I feel is an important aspect of social justice,” using the scale 1—“Definitely NOT true for social justice” to 7—“Definitely true for social justice.” Statement numbers correspond to the points in Figure 1.
Figure 1 shows the five clusters that described domains of social justice. These clusters ranged in size from 9 to 11 statements. Mean ratings ranged from 5.22 to 6.04 (out of 7) based on the prompt, “This is something that I feel is an important aspect of social justice.” A description of each cluster is summarized in the next section. See Table 2 for a full list of statements within each cluster.
Sections
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Metadata
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