Acute surgical wound-dressing procedure: Description of the steps involved in the development and validation of an observational metric

The aim of this study was to develop an observational metric that could be used to assess the performance of a practitioner in completing an acute surgical wound-dressing procedure using aseptic non-touch technique (ANTT). A team of clinicians, academics, and researchers came together to develop an observational metric using an iterative six-stage process, culminating in a Delphi panel meeting. A scoping review of the literature provided a background empirical perspective relating to wound-dressing procedure performance. Video recordings of acute surgical wound-dressing procedures performed by nurses in clinical (n = 11) and simulated (n = 3) settings were viewed repeatedly and were iteratively deconstructed by the metric development group. This facilitated the identification of the discrete component steps, potential errors, and sentinel (serious) errors, which characterise a wound dressing procedure and formed part of the observational metric. The ANTT wound-dressing observational metric was stress tested for clarity, the ability to be scored, and interrater reliability, calculated during a further phase of video analysis. The metric was then subjected to a process of cyclical evaluation by a Delphi panel (n = 21) to obtain face and content validity of the metric. The Delphi panel deliberation verified the face and content validity of the metric. The final metric has three phases, 31 individual steps, 18 errors, and 27 sentinel errors. The metric is a tool that identifies the standard to be attained in the performance of acute surgical wound dressings. It can be used as both an adjunct to an educational programme and as a tool to assess a practitioner's performance of a wound-dressing procedure in both simulated and clinical practice contexts

Short versus long term benefits and the evolution of cooperation in the prisoner's dilemma game

In this paper I investigate the evolution of cooperation in the prisoner's dilemma when individuals change their strategies subject to performance evaluation of their neighbours over variable time horizons. In the monochrome setting, in which all agents per default share the same performance evaluation rule, weighing past events strongly dramatically enhances the prevalence of cooperators. For co-evolutionary models, in which evaluation time horizons and strategies can co-evolve, I demonstrate that cooperation naturally associates with long-term evaluation of others while defection is typically paired with very short time horizons. Moreover, considering the continuous spectrum in between enhanced and discounted weights of past performance, cooperation is optimally supported when cooperators neither give enhanced weight to past nor more recent events, but simply average payoffs. Payoff averaging is also found to emerge as the dominant strategy for cooperators in co-evolutionary models, thus proposing a natural route to the evolution of cooperation in viscous populations

Combination Forecasts of Bond and Stock Returns: An Asset Allocation Perspective

We investigate the out-of-sample forecasting ability of the HML, SMB, momentum, short-term and long-term reversal factors along with their size and value decompositions on U.S. bond and stock returns for a variety of horizons ranging from the short run (1 month) to the long run (2 years). Our findings suggest that these factors contain significantly more information for future bond and stock market returns than the typically employed financial variables. Combination of forecasts of the empirical factors turns out to be particularly successful, especially from an an asset allocation perspective. Similar findings pertain to the European and Japanese markets

Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications

Cognitive strategic groups and long-run efficiency evaluation : the case of Spanish savings banks

In the framework of Cognitive Approach, this paper proposes a new method to identify strategic groups (SG) using Data Envelopment Analysis (DEA) methods. Two assumptions are maintained in the SG literature: first, firms grouped together value inputs and outputs similarly, and, second, some degree of stability in those valuations should be identified. Virtual weights obtained from DEA are extremely useful in the valuation of the strategic variables, but a problem emerges when longitudinal analysis is performed. This problem is addressed by defining a long run DEA evaluation. SGs are determined by means of Cluster Analysis, using virtual outputs and virtual inputs as variables and Spanish savings banks as observations. The traditional method of determining SGs by clustering on the original variables is also applied and the results are compared. It is shown that the long run DEA weights approach has advantages over the traditional methodology

Faculty Research in Progress, 2018-2019

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty\u27s research continues to find its way into the very best industrial relations, social science and statistics journal

Tone from the Top in Risk Management: A Complementarity Perspective on How Control Systems Influence Risk Awareness

Prompted by the weaknesses of standardized risk management approaches in the aftermath of the 2008 financial crisis, scholars, regulators, and practitioners alike emphasize the importance of creating a risk-aware culture in organizations. Recent insights highlight the special role of tone from the top as crucial driver of risk awareness. In this study, we take a systems-perspective on control system design to investigate the role of tone from the top in creating risk awareness. In particular, we argue that both interactive and diagnostic use of budgets and performance measures interact with tone from the top in managing risk awareness. Our results show that interactive control strengthens the effect of tone from the top on risk awareness, while tone from the top and diagnostic control are, on average, not interrelated with regard to creating risk awareness. To shed light on the boundary conditions of the proposed interdependencies, we further investigate whether the predicted interdependencies are sensitive to the level of perceived environmental uncertainty. We find that the effect of tone from the top and interactive control becomes significantly stronger in a situation of high perceived environmental uncertainty. Most interestingly, tone from the top and diagnostic control are complements with regard to risk awareness in settings of low perceived environmental uncertainty and substitutes at high levels of perceived environmental uncertainty.Series: Department of Strategy and Innovation Working Paper Serie

Evaluation of the NAS-ILAB Matrix for Monitoring International Labor Standards: Project Report

The Bureau of International Labor Affairs (ILAB) engaged the National Research Council of the National Academy of Sciences (NAS) to recommend a method to monitor and evaluate labor conditions in a given country. The method focuses on 5 labor standards: freedom of association and collective bargaining, forced or compulsory labor, child labor, discrimination, and acceptable conditions of work