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Data Driven Monitoring in Community Based Management of SAM children using Psychometric Techniques: An Operational Framework

By ANKUR JOSHI, Abhijit P Pakhare, Sivaja K Nair, Revadi G, MANOJ CHOUHAN, DEEPAK PANDEY, ARUN M KOKANE

Posted 22 Jun 2021
medRxiv DOI: 10.1101/2021.06.16.21258807

Background- The success of the Community Based Management of Severe Malnutrition (CSAM)programme, largely depends on the knowledge and skills of Front-Line Workers (FLWs).A robust supportive supervision system in CSAM should be tailored to individualistic learning needs by distinguishing the FLWs as per their ability and simultaneously identifying the task domains to be emphasized more in supervisory visits.This paper details the ability assessment strategy developed and employed in the selected geographical locations in state of Madhya Pradesh (Central India) among the 197 Anganwadi workers (FLWs involved in CSAM implementation) Methodology-. A 25 items tool was developed based on an analytical construct for ability estimation through Rasch Analysis (RA). RA models the probability of right/wrong answer as a function of person(participants) and item (questions) parameters and calculates the item difficulty in relation with person ability on same unidimensional linear scale. The fitting of the data to Rasch model (Rasch diagnostic) was tested by both numeric (Anderson LR and Wald test) and graphical method. Suitable visualization like Item Characteristic Curve (ICC) and Person Item Map (PIM) were plotted in RA. Further a quadratic allocation of all AWWs into 4 quadrants were done as per the ability estimation (Rasch score) and adjusted numbers of SAM/MAM children in her center. Results-. . The item easiness parameter ({beta}) value related to Diarrhoeal assessment was lowest (-2.32, -2.91 to -1.73) and related to peer assessment consequential action (2.009, 1.669- 2.348)) was highest (most difficult). Anderson LR test (LR=31.32, df=24, p=0.079) showed the absence of global outliers. Quadrant analysis using the permutations of ability score and adjusted burden of malnutrition further mapped 41/197 (20.8%) FLWs to low ability -high burden quadrant and 44/197(25%) as low ability low burden quadrant. Conclusion- RASCH assessment may address the innate challenges to maintain homogeneity, discrimination capacity and linearity in a raw score-based measurement construct. The monitoring strategy developed on this thus may offer a judicious, pragmatic and thematic approach to supportive supervision in CSAM program. Keywords: Severe Acute Malnutrition, RASCH assessment, data driven monitoring

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