Back

Development and Validation of the ICIS (Inclusive Capacity and Inclusion Intent Scale): A Psychometric Scale to Measure Individual Inclusion Intent and Inclusive Capacity

Gonzalez Ballesteros, L. M.; Castellanos Roncancio, C. A.; Mojica Ospina, L. E.

2025-08-18 psychiatry and clinical psychology
10.1101/2025.08.14.25333692 medRxiv
Show abstract

Violence and social exclusion are interconnected issues driven by factors such as inequality, poverty, and weak state institutions, leading to the marginalization of vulnerable groups. Being able to measure individuals ability to include others could be useful in guiding social intervention programs and improving resource allocation, especially in vulnerable populations. Using a mixed methods study we developed, pilot tested and evaluated a research-based scale to assess individuals inclusion intent as a proxy for inclusion capacity. The psychometric properties of this scale were also assessed. We found strong internal consistency for the entire scale ( = 0.89) and for two of the three domains (Attitudes = 0.84, Perceived Control = 0.83). Although the Subjective Norm domain showed lower reliability ( = 0.51), likely due to the limited number of items. These results provide preliminary evidence of construct validity whilst suggesting areas for enhancement, particularly regarding the measurement of the subjective norms related to inclusion. The new ICIS scale provides an innovative and reliable measure of inclusion intent, serving as an indicator of inclusion capacity. It is a useful tool for assessing individual inclusion abilities across various fields, including research, clinical practice, and policy development or implementation. Furthermore, it could be used to assess the general inclusion capacity of a community. Due to its recent introduction, direct comparisons with prior studies remain challenging. Further research is required to adjust any underperforming aspects of the scale and assess its validity in wider populations.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.