The Internalized Ableism Inventory: Scale development using a hybrid artificial intelligence and community-based...

Area of Science:

• Psychology
• Disability Studies
• Artificial Intelligence in Research

Background:

• Internalized ableism is a significant issue impacting disabled individuals.
• Existing measures may not fully capture the nuances of internalized ableism.
• There is a need for psychometrically sound scales developed through inclusive methodologies.

Purpose of the Study:

• To develop and psychometrically validate a scale measuring internalized ableism.
• To integrate artificial intelligence (AI) and community-based participatory research (CBPR) in scale development.
• To create the Internalized Ableism Inventory (IAI).

Main Methods:

• Utilized OpenAI's GPT-4 large language model for initial item generation.
• Incorporated disabled community stakeholders for item refinement and AI-prompting.
• Conducted exploratory and confirmatory factor analyses on data from 409 disabled adults.

Main Results:

• Developed a 51-item scale with eight subscales, demonstrating excellent factor structure fit.
• Achieved high reliability with Cronbach's alpha ranging from .85 to .98.
• Established convergent validity with measures of internalized stigma and disability-related frustration.

Conclusions:

• The Internalized Ableism Inventory is the first comprehensive, psychometrically sound scale developed using AI and CBPR.
• The novel methodology has the potential to modernize psychological scale development.
• The IAI provides a valuable tool for research and clinical practice concerning internalized ableism.