Physician-Specific Symptoms of Burnout Compared to a Non-Physicians Group

Physician burnout is a systemic problem in health care due to its high prevalence and its negative impact on professional functioning and individual well-being. While unique aspects of the physician role contributing to the development burnout have been investigated recently, it is currently unclear whether burnout manifests differently in physicians compared to the non-physician working population. We conducted an individual symptom analysis of burnout symptoms comparing a large sample of physicians with a non-physician group. In this cross-sectional online study, burnout was assessed with the Maslach Burnout Inventory—General Survey. We matched physicians with non-physicians regarding their age, gender, educational level, occupational status, and total burnout level using a “nearest neighbour matching” procedure. We then conducted a series of between-groups comparisons. Data of 3846 (51.0% women) participants including 641 physicians and 3205 non-physicians were analysed. The most pronounced difference was that physicians were more satisfied with their work performance (medium effect size (r = 0.343). Our findings indicate minor yet significant differences in burnout phenomenology between physicians and non-physicians. This demonstrates unique aspects of physician burnout and implies that such differences should be considered in occupational research among physicians, particularly when developing burnout prevention programs for physicians

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

A new demo modelling tool that facilitates model transformations

The age of digitization requires rapid design and re-design of enterprises. Rapid changes can be realized using conceptual modelling. The design and engineering methodology for organizations (DEMO) is an established modelling method for representing the organization domain of an enterprise. However, heterogeneity in enterprise design stakeholders generally demand for transformations between conceptual modelling languages. Specifically, in the case of DEMO, a transformation into business process modelling and notation (BPMN) models is desirable to account to both, the semantic sound foundation of the DEMO models, and the wide adoption of the de-facto industry standard BPMN. Model transformation can only be efficiently applied if tool support is available. Our research starts with a state-of-the-art analysis, comparing existing DEMO modelling tools. Using a design science research approach, our main contribution is the development of a DEMO modelling tool on the ADOxx platform. One of the main features of our tool is that it addresses stakeholder heterogeneity by enabling transformation of a DEMO organization construction diagram (OCD) into a BPMN collaboration diagram. A demonstration case shows the feasibility of our newly developed tool.http://www.springer.com/series/7911hj2021Industrial and Systems Engineerin

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep