Browsing by Author "Nixon, James"

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    Exploring the impact of safety culture on incident reporting: lessons learned from machine learning analysis of NHS England staff survey and incident data
    (Elsevier, 2023-07-13) Kaya, Gulsum Kubra; Ustebay, S.; Nixon, James; Pilbeam, Colin
    Safety culture is one of the key factors contributing to safety, even though limited evidence supports its impact on safety outcomes. This study uses supervised machine learning algorithms to explore the association between safety culture and incident reporting. The study used National Health Service (NHS) England annual staff survey data as a proxy of safety culture to predict eighteen incident reporting variables. The study did not achieve high accuracy rates in the prediction models. The highest association was found between safety culture and the number of incidents reported in class low, medium and high. LightGBM was the best-performed algorithm. SHAP plots were used to explain the model. Findings suggest that compassionate culture, violence and harassment and work pressure are critical in predicting the number of incidents reported. More specifically, the violence and harassment had a more significant impact on predicting the number of incidents reported in class high than in class medium and low. The involvement had more effect on predicting class low. The results demonstrated different behaviours in predicting different incident reporting classes. The findings facilitate lessons learned from staff surveys and incident reporting data in NHS England. Consequently, the findings can contribute to improving the safety culture in hospitals.
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    Understanding the human performance envelope using electrophysiological measures from wearable technology
    (Springer, 2017-09-13) Nixon, James; Charles, Rebecca
    In this article, we capture electrophysiological measures from a new wearable technology to understand the human performance envelope. Using the NASA Multi-Attribute Task Battery (MATB II), participants completed tasks associated with flight control which included communication, tracking and system and resource monitoring. Electrophysiological measures relating to cardiac activity and respiration were taken using the new wearable technology. Our results show significant differences in both heart rate and respiration rate in response to different taskloads and that higher taskloads were associated with higher mental workload. Frequency measures of heart rate variability discriminated different task types but not taskloads. This finding may be related to differences in task complexity being more important than the number events which we have used to manipulate taskload. We suggest that this new generation of wearable sensors could be used to inform operator locus in a human performance envelope, indicating when assistance by the aircraft or another crew member may be necessary to maintain safe and efficient performance.