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dc.contributor.authorNassimbwa, Florence
dc.date.accessioned2024-07-10T09:16:28Z
dc.date.available2024-07-10T09:16:28Z
dc.date.issued2023-10
dc.identifier.citationNassimbwa, F. (2023). Human factors and accidents of aviation operations in eastern Africa.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12504/1966
dc.descriptionxvii, 195 p. : ill. (some col.) ;en_US
dc.description.abstractSafety performance is much poorer in developing countries when one compares to other countries worldwide, despite the fact that the aviation crews in these countries are trained with the aim of minimizing aviation accidents. The accidents are due to errors caused by failure in the human biological functionality which is totally related to our anthropometric limitations, physiology and anatomy, among others. These include; oxygen carrying capabilities/ hypoxia, hyperventilation, blind spots in the eye, disorientation in relation to the human's position in space, motion sickness, illusions, barotrauma, decompression sickness, circadian dysrhythmias, drugs/medication, sleep disorders, weight, fatigue, stress and many others. Therefor this study set out to investigate the human factor risks and accidents in Eastern Africa region aviation operations. A cross sectional research design was applied and quantitative data collected using a survey. The sampling procedure included both purposive and random sampling techniques used to draw a representative sample of aviation stake holders. Data from 42 Ugandan and Kenyan accident and incident final investigation reports from 2000- 2017 was analyzed. Results indicated that skill based errors in Kenya were 44% and in Uganda 50%, whereas decision errors in Kenya were 45% , Uganda 25% and perceptual errors and violation in Uganda were 25% and in Kenya 11% while exceptional violations were Kenya 77% and Uganda 81%. MLogit model showed significance at P<0.001, in the manner with which one flies, exceptional violation, supervisory violations and organizational processes. Examining of the current human factor risks in the region’s aviation operations showed that there are risks in quality control analysis (P= 0.023) and troubleshooting abilities (P= 0.02). Quality control analysis and troubleshooting abilities had a significant effect on the ability to predict skills required for the job (P < 0.05). The significant aeromedical factors included sleep (P=0.005), high levels of anxiety (P= 0.021), shortness of breath (P=0.011) and cigarette smoking (P<0.001). The Geographical Information Systems (GIS) tool captured latent human factors risks through ranking 40 randomly selected airports in the region, while the distress thermometer captured active risks which included health, stress (domestic and work related), fitness, deadlines and time pressure, sleep disorders, fatigue and pain. It can be concluded that both Kenya and Uganda had high levels of unsafe acts, although Kenya had a higher percentage. Four significant aeromedical factors were captured in the existing aeromedical factors in the region. Consequently, training and its evaluation plus the use of a regional adaptive curriculum that increases individual’s skills and reduces the emerging unsafe acts, and Uganda publicizing final accident and incident investigation reports on the responsible ministry (Works and Transport) website are recommended. Using the GIS tool and the distress thermometer to detect latent and active human factor risks pre-flight and on ground to assess the state of the crew members and environment before one embarks on a task is also recommended. Map of airports and aerodromes with high human factors risks in Kenya Map of airports and aerodromes with high human factors risks in Kenya Map of airports and aerodromes with high human factors risks in Kenya.en_US
dc.language.isoenen_US
dc.publisherKyambogo University [unpublished work]en_US
dc.subjectHuman factorsen_US
dc.subjectAccidentsen_US
dc.subjectAviation operationsen_US
dc.subjectEastern Africaen_US
dc.titleHuman factors and accidents of aviation operations in eastern Africaen_US
dc.typeThesisen_US


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