Combining Child Functioning Data with Learning and Support Needs Data to Create Disability-Identification Algorithms...

Area of Science:

• Public Health
• Education Policy
• Disability Studies

Background:

• Accurate disability identification is crucial for allocating inclusive education grants in Fiji.
• Existing data from the Child Functioning Module (CFM) alone is insufficient for precise disability identification.
• Fiji's Education Management Information System (FEMIS) requires disability disaggregation for grant eligibility.

Purpose of the Study:

• To assess if combining CFM data with learning and support needs (LSN) data improves disability identification accuracy.
• To explore the utility of environmental factors in identifying children with disabilities.
• To enhance the accuracy of disability data within FEMIS for inclusive education.

Main Methods:

• A diagnostic accuracy study incorporating the UNICEF/Washington Group Child Functioning Module (CFM).
• Administered a survey to teachers on children's LSN, including personal assistance, learning adaptations, and assistive technology.
• Utilized descriptive statistics and correlation analyses to examine relationships between functioning and LSN.

Main Results:

• CFM data effectively distinguishes between different disability domains.
• LSN data significantly strengthens the accuracy of disability severity assessment.
• LSN data is crucial for identifying children with disabilities among those reporting some difficulty on the CFM.

Conclusions:

• Combining CFM activity/participation data with LSN environmental factors can improve domain-specific disability identification.
• The addition of LSN data effectively identifies children with disabilities within the 'some difficulty' category of the CFM.
• This integrated approach enhances the accuracy of disability data for inclusive education policy and resource allocation.