International Journal of Academic Research in Business and Social Sciences

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Influence Of M&E Training On Sustainability Of Disaster Emergency Preparedness Program In Uasin Gishu County

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Disasters have become a typical occurrences all around the globe. Certainly, their frequency has grown as economic growth, technological sophistication, and vulnerability to disadvantaged groups has risen. Natural calamities are predicted to cause the deaths of one million people worldwide per decade. There is minimal evidence that M&E of disaster response operations leads to better results in terms of more effective practice. As such, the aim of this research was be to determine the influence of M&E training on sustainability of disaster emergency preparedness program in Uasin Gishu County. This study adopted theory of Sustainability. This study used an explanatory research design. Because of the limited number of workers, a census survey will be used; this research target population will be 52 workers of the Disaster Emergency Preparedness Program and M&E in Uasin Gishu County. Questionnaires was used to collect data. Data analysis process will begin with questionnaire editing. Collected data were coded into Statistical package for social science (SPSS) for analysis. Descriptive statistics such as frequency distribution, percentages, means and standard deviations were calculated, and data presented in form of tables. Inferential statistics used were regression model. The study findings revealed that M&E training has a positive significant effect on Sustainability of disaster emergency preparedness programs (?2=0.363, p=0.000<0.05). The study also concluded that there exist a strong, positive and statistically correlation between M&E training on sustainability of disaster emergency preparedness program in Uasin Gishu County Kenya. M&E of disaster response efforts leads to better outcomes in terms of improved practice. Determine the extent recovery program activities have built sustainability and resilience in a community post-disaster. Assess community resilience in disasters by developing linked conceptual and computational models of community functioning and resilience after a disaster.
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