Pressure ulcer management in Oman: nurses’ knowledge and views

Background: Pressure ulcers (PrUs) have a significant impact on health system expenditure and patient’s quality of life. It is a global problem. Many studies were undertaken in regard to PrU prevention and management. In Oman, no studies have been conducted to investigate nurses’ knowledge on prevention and management of PrUs. The purpose of this descriptive sequential explanatory mixed-method study was to explore the nurses’ level of knowledge in relation to prevention and management of PrUs in Oman. Methods: A mixed method design was used and the study was conducted over two Phases. In Phase I, a questionnaire was developed to explore nurses’ knowledge on PrU, policy, and resources. The main section of the questionnaire was the Pieper-Zulkowski Pressure Ulcer knowledge test (PZ-PUKT) which tests the knowledge on PrU. Another two sections were developed including questions about wound policy and resources available for PrU prevention and management in Oman. The questionnaire was distributed to nurses who were working in surgical, medical, orthopaedic, CCU, and ICU wards/units in seven hospitals. In Phase II study, semi-structured qualitative interviews were conducted with 16 of the questionnaire respondents. Interviews took approximately 30 minutes, were recorded and transcribed verbatim. Qualitative data were analysed using the Knowledge, Attitudes and Practice (KAP) model as the a priori framework. Results: In Phase I, 478 questionnaires were analysed. The knowledge test results showed the overall mean percent score for correctly answered questions was 51% suggesting a low level of knowledge. There was a significant relationship between nurses’ knowledge and age (P=0.001) and between knowledge and years of experience (P=0.001) with knowledge increasing with age and years of experience. In Phase II, four themes were identified from the interviews: knowledge, attitude, and practice (framework themes) and perception of role. Findings indicated positive and negative attitudes towards the care of PrUs. Some nurses stated feeling rewarded when they see wounds improving while others said they could not work with patients independently because they lacked the knowledge and the skills needed. There was variation in the management of PrU between hospitals. Both studies indicated that the wound management policy did not include enough information to guide nurses. Conclusion: Overall the nurses’ level of knowledge on PrU was relatively low. Most nurses were not familiar with wound management policy or different PrU prevention and management strategies. Nurses are aware of the risk of PrUs and try their best to manage them with the available resources however more training is required.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.