Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia.
Int J Environ Res Public Health. 2021 Sep 6;18(17):9413. doi: 10.3390/ijerph18179413.
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.
斐济教育管理信息系统(FEMIS)需要进行残疾分类,以确定是否有资格获得全纳教育补助金。仅使用联合国儿童基金会/华盛顿小组儿童功能模块(CFM)的数据,不足以准确识别残疾情况。本研究探讨了将 CFM 的活动和参与数据与特定于学习和支持需求(LSN)的环境因素数据相结合,是否能更准确地识别残疾儿童。在更广泛的诊断准确性研究中,向教师们发放了一份关于儿童 LSN(个人援助、学习适应或评估和辅助技术)问题的调查。使用描述性统计和相关性分析来分析功能与 LSN 之间的关系。CFM 数据在区分残疾领域方面很有用,LSN 数据在增强残疾严重程度数据的准确性方面很有用,并且在识别那些在 CFM 上报告有一些困难的儿童中哪些是残疾儿童方面非常有用。通过算法将 CFM 的活动和参与数据与环境因素数据相结合,可能会提高特定领域残疾识别的准确性。在 CFM 上报告有一些困难的儿童中,通过添加 LSN 数据,可以有效地识别出残疾儿童。