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Combining Child Functioning Data with Learning and Support Needs Data to Create Disability-Identification Algorithms in Fiji’s Education Management Information System

Author

Listed:
  • Beth Sprunt

    (Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia)

  • Manjula Marella

    (Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia)

Abstract

Disability disaggregation of Fiji’s Education Management Information System (FEMIS) is required to determine eligibility for inclusive education grants. Data from the UNICEF/Washington Group Child Functioning Module (CFM) alone is not accurate enough to identify disabilities for this purpose. This study explores whether combining activity and participation data from the CFM with data on environmental factors specific to learning and support needs (LSN) more accurately identifies children with disabilities. A survey on questions related to children’s LSN (personal assistance, adaptations to learning, or assessment and assistive technology) was administered to teachers within a broader diagnostic accuracy study. Descriptive statistics and correlations were used to analyze relationships between functioning and LSN. While CFM data are useful in distinguishing between disability domains, LSN data are useful in strengthening the accuracy of disability severity data and, crucially, in identifying which children have disability amongst those reported as having some difficulty on the CFM. Combining activity and participation data from the CFM with environmental factors data through algorithms may increase the accuracy of domain-specific disability identification. Amongst children reported as having some difficulty on the CFM, those with disabilities are effectively identified through the addition of LSN data.

Suggested Citation

  • Beth Sprunt & Manjula Marella, 2021. "Combining Child Functioning Data with Learning and Support Needs Data to Create Disability-Identification Algorithms in Fiji’s Education Management Information System," IJERPH, MDPI, vol. 18(17), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9413-:d:630146
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    References listed on IDEAS

    as
    1. Beth Sprunt & Barbara McPake & Manjula Marella, 2019. "The UNICEF/Washington Group Child Functioning Module—Accuracy, Inter-Rater Reliability and Cut-Off Level for Disability Disaggregation of Fiji’s Education Management Information System," IJERPH, MDPI, vol. 16(5), pages 1-22, March.
    2. Rosamond H. Madden & Nick Glozier & Nicola Fortune & Maree Dyson & John Gilroy & Anita Bundy & Gwynnyth Llewellyn & Luis Salvador-Carulla & Sue Lukersmith & Elias Mpofu & Richard Madden, 2015. "In Search of an Integrative Measure of Functioning," IJERPH, MDPI, vol. 12(6), pages 1-18, May.
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    Cited by:

    1. Surbhi Bhatia Khan & Mohammed Alojail & Moteeb Al Moteri, 2023. "Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization," Mathematics, MDPI, vol. 12(1), pages 1-20, December.
    2. Dorothy Boggs & Hannah Kuper & Islay Mactaggart & Tess Bright & GVS Murthy & Abba Hydara & Ian McCormick & Natalia Tamblay & Matias L. Alvarez & Oluwarantimi Atijosan-Ayodele & Hisem Yonso & Allen Fos, 2022. "Exploring the Use of Washington Group Questions to Identify People with Clinical Impairments Who Need Services including Assistive Products: Results from Five Population-Based Surveys," IJERPH, MDPI, vol. 19(7), pages 1-17, April.

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