Quantitative Intersectionality Scoring System (QISS): Opportunities for Enhancing Predictive Modeling, Comparative...

Area of Science:

• Social Sciences
• Public Health
• Quantitative Methods

Background:

• Intersectionality highlights how overlapping social identities (race, gender, class, etc.) shape experiences.
• Existing research often uses qualitative methods, limiting quantitative applications like predictive modeling and policy development.
• There is a need for data-driven approaches to analyze complex, intersecting social determinants.

Purpose of the Study:

• Introduce the Quantitative Intersectionality Scoring System (QISS) for numerical analysis of intersecting identities.
• Demonstrate QISS's utility in enhancing predictive validity and enabling systematic, data-driven studies of intersectional effects.
• Showcase QISS's potential for improving policy evaluation and promoting social justice and health equity.

Main Methods:

• Development of the Quantitative Intersectionality Scoring System (QISS) to assign numerical scores to intersecting social identities.
• Application of QISS in quantitative models to analyze the complex interplay of multiple social determinants.
• Illustrative examples, such as socioeconomic mobility's impact on life expectancy across different intersectional groups.

Main Results:

• QISS enables a more systematic and data-driven analysis of intersectional effects.
• QISS-based models can provide more precise and reliable forecasts of social and health outcomes.
• The system allows for dynamic assessment of needs and uncertainties for policymakers and service providers.

Conclusions:

• QISS significantly enhances the utility and predictive validity of quantitative models by capturing intersecting social determinants.
• QISS facilitates more quantified, nuanced policy evaluations and responsive interventions.
• Further research is needed to validate QISS using robust quantitative methods for improved accuracy and policy effectiveness.