Authentic leadership and thriving among nurses: the mediating role of empathy

Thriving at work is defined as a psychological state in which employees experience both a sense of vitality and a sense of learning at work (Porath, Spreitzer, Gibson, & Garnett, 2012) and is an important indicator of employees’ well-being (Shirom, Toker, Berliner, Shapira, & Melamed, 2008). Previous research has shown that thriving is related to positive work outcomes such as innovative work behavior (Carmeli & Spreitzer, 2009). So far, thriving has not been studied in health care workers, and little is known about its antecedents or psychological mechanisms which may foster the development of thriving. Given these research voids and as nurses’ well-being at work (Shirom, et al., 2008), their competences (e.g. Cowan, Wilson-Barnett, Norman, & Murrelss, 2008) and the quality of leadership (Cummings et al., 2010) are key assets for health care organizations, we hypothesize in the current study that thriving in nurses is positively associated to authentic leadership of the headnurse. Moreover we expect that this relation will be mediated by nurses’ empathy. Cross sectional data were collected in a Flemish hospital nurse sample (N = 360) by means of a self-report questionnaire, including some validated scales. Data were analyzed by hierarchical linear regression, the multi-step procedure of Baron and Kenny (1986), bootstrapping and a Sobel-test. We took account of some control variables. General results showed a partial mediation of empathy on the positive relation between authentic leadership and thriving. More specifically, we found that nurses’ empathy fully mediated the relationship between authentic leadership and their level of vitality at work. In contrast, empathy did not mediate the relationship between authentic leadership and learning. Explanations and implications of these results and research limitations will be discussed at the time of the conference

Enabling automated device size selection for transcatheter aortic valve implantation

The number of transcatheter aortic valve implantation (TAVI) procedures is expected to increase significantly in the coming years. Improving efficiency will become essential for experienced operators performing large TAVI volumes, while new operators will require training and may benefit from accurate support. In this work, we present a fast deep learning method that can predict aortic annulus perimeter and area automatically from aortic annular plane images. We propose a method combining two deep convolutional neural networks followed by a postprocessing step. The models were trained with 355 patients using modern deep learning techniques, and the method was evaluated on another 118 patients. The method was validated against an interoperator variability study of the same 118 patients. The differences between the manually obtained aortic annulus measurements and the automatic predictions were similar to the differences between two independent observers (paired diff. of 3.3 +/- 16.8 mm(2) vs. 1.3 +/- 21.1 mm(2) for the area and a paired diff. of 0.6 +/- 1.7 mm vs. 0.2 +/- 2.5 mm for the perimeter). The area and perimeter were used to retrieve the suggested prosthesis sizes for the Edwards Sapien 3 and the Medtronic Evolut device retrospectively. The automatically obtained device size selections accorded well with the device sizes selected by operator 1. The total analysis time from aortic annular plane to prosthesis size was below one second. This study showed that automated TAVI device size selection using the proposed method is fast, accurate, and reproducible. Comparison with the interobserver variability has shown the reliability of the strategy, and embedding this tool based on deep learning in the preoperative planning routine has the potential to increase the efficiency while ensuring accuracy

Scanning electron microscopic study of different hair types in various breeds of rabbits

The microarchitecture of the cover hairs, wool hairs and tactile (sinus) hairs of feral, New Zealand White and Angora rabbits was studied by means of scanning electron microscopy. The morphology and variability of the cuticular scale patterns, hair cortex, medullary arrangement and profile of the hairs are described, illustrated and compared with findings resulting from conventional light microscopy, cuticular casting and medullary impregnation. All parameters examined in cover hairs presented a considerable variation along the length of the hair shaft. In wool hairs, in contrast, only the cuticular scale pattern was subject to manifest segmental variation, whereas the shaft diameter, cortical profile and medullar composition changed little over the entire length of the hair. The tactile hairs of the head were characterised by a round profile of the hair shaft, a cylindrical central medullar canal, and a thick cortex covered by cuticular scales that were arranged in a waved pattern and oriented transversally in relation to the longitudinal axis of the hair. It was concluded that the scanning electron microscopic observation of hair samples is a fast and valuable method for identifying hair types with useful applications in different disciplines such as mammalian biology, the textile industry and forensic medicine

Curriculum deep reinforcement learning with different exploration strategies : a feasibility study on cardiac landmark detection

Transcatheter aortic valve implantation (TAVI) is associated with conduction abnormalities and the mechanical interaction between the prosthesis and the atrioventricular (AV) conduction path cause these life-threatening arrhythmias. Pre-operative assessment of the location of the AV conduction path can help to understand the risk of post-TAVI conduction abnormalities. As the AV conduction path is not visible on cardiac CT, the inferior border of the membranous septum can be used as an anatomical landmark. Detecting this border automatically, accurately and efficiently would save operator time and thus benefit pre-operative planning. This preliminary study was performed to identify the feasibility of 3D landmark detection in cardiac CT images with curriculum deep Q-learning. In this study, curriculum learning was used to gradually teach an artificial agent to detect this anatomical landmark from cardiac CT. This agent was equipped with a small field of view and burdened with a large ac tion-space. Moreover, we introduced two novel action-selection strategies: α-decay and action-dropout. We compared these two strategies to the already established ε-decay strategy and observed that α-decay yielded the most accurate results. Limited computational resources were used to ensure reproducibility. In order to maximize the amount of patient data, the method was cross-validated with k-folding for all three action-selection strategies. An inter-operator variability study was conducted to assess the accuracy of the metho