A pilot study of operating department practitioners undertaking high-risk learning: a comparison of experiential, part-task and hi-fidelity simulation teaching methods

Health care learners commonly rely on opportunistic experiential learning in clinical placements in order to develop cognitive and psychomotor clinical skills. In recent years there has been an increasing effort to develop effective alternative, non-opportunistic methods of learning, in an attempt to bypass the questionable tradition of relying on patients to practice on. As part of such efforts, there is an increased utilisation of simulation-based education. However, the effectiveness of simulation in health care education arguably varies between professions (Liaw, Chan, Scherpbier, Rethans, & Pua, 2012; Oberleitner, Broussard, & Bourque, 2011; Ross, 2012). This pilot study compares the effectiveness of three educational (or ‘teaching’) methods in the development of clinical knowledge and skills during Rapid Sequence Induction (RSI) of anaesthesia, a potentially life-threatening clinical situation. Students of Operating Department Practice (ODP) undertook either a) traditional classroom based and experiential learning, b) part-task training, or c) fully submersive scenario-based simulated learning

Nonprofit Management Tools and Trends 2014

The heightened importance of strong nonprofit management calls attention to a wide range of management practices that we call tools. Despite their importance, to date there has been no systematic attempt to understand what tools are being used or how effective they are. This report aims to fill that knowledge gap. It creates a "consumer report" for nonprofit leaders seeking to apply one or more of 25 popular tools to the challenges at hand. These tools can help organizations live up to their missions and meet funders' expectations for results. Many of the tools on our list, such as scenario planning and benchmarking, migrated from the business world. Others, such as funding models and constituent engagement, evolved specifically with nonprofit needs in mind.To understand how many tools a typical nonprofit uses, for what purposes, and how they perform, The Bridgespan Group developed a survey of the top nonprofit tools and trends in the social sector, nominated by a panel of more than two dozen practitioners, funders, and intermediaries. Overall findings confirm nonprofits' widespread use of management tools and their interest in using more in the future. The survey also provides insights into how well those tools help leaders respond to trends in the sector. It is our intent that this report will be repeated at intervals and should serve to stimulate questions, test practices, spark experiments, and ultimately help managers to get better results

Does managerial turnover affect football club share prices?

This paper analyses the 53 managerial sackings and resignations from 16 stock exchange listed English football clubs during the nine seasons between 2000/01 and 2008/09. The results demonstrate that, on average, a managerial sacking results in a post-announcement day market-adjusted share price rise of 0.3%, whilst a resignation leads to a drop in share price of 1% that continues for a trading month thereafter, cumulating in a negative abnormal return of over 8% from a trading day before the event. These findings are intuitive, and suggest that sacking a poorly performing manager may be welcomed by the markets as a possible route to better future match performance, while losing a capable manager through resignation, who typically progresses to a superior job, will result in a drop in a club’s share price. The paper also reveals that while the impact of managerial departures on stock price volatilities is less clear-cut, speculation in the newspapers is rife in the build-up to such an event

The performance of football club managers: skill or luck?

This paper evaluates the extent to which the performance of English Premier League football club managers can be attributed to skill or luck when measured separately from the characteristics of the team. We first use a specification that models managerial skill as a fixed effect and we examine the relationship between the number of points earned in league matches and the club’s wage bill, transfer spending, and the extent to which they were hit by absent players through injuries, suspensions or unavailability. We next implement a bootstrapping approach to generate a simulated distribution of average points that could have taken place after the impact of the manager has been removed. The findings suggest that there are a considerable number of highly skilled managers but also several who perform below expectations. The paper proceeds to illustrate how the approach adopted could be used to determine the optimal time for a club to part company with its manager. We are able to identify in advance several managers who the analysis suggests could have been fired earlier and others whose sackings were hard to justify based on their performances

Autonomous learning for face recognition in the wild via ambient wireless cues

Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak. We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort

SCAN : learning speaker identity from noisy sensor data

Sensor data acquired from multiple sensors simultaneously is featuring increasingly in our evermore pervasive world. Buildings can be made smarter and more efficient, spaces more responsive to users. A fundamental building block towards smart spaces is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit the unique vocal features as people interact with one another. As an example, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation (e.g. through a calendar or MAC address), can we learn to associate a specific identity with a particular voiceprint? Obviously enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. To address this problem, the standard approach is to perform a clustering step (e.g. of audio data) followed by a data association step, when identity-rich sensor data is available. In this paper we show that this approach is not robust to noise in either type of sensor stream; to tackle this issue we propose a novel algorithm that jointly optimises the clustering and association process yielding up to three times higher identification precision than approaches that execute these steps sequentially. We demonstrate the performance benefits of our approach in two case studies, one with acoustic and MAC datasets that we collected from meetings in a non-residential building, and another from an online dataset from recorded radio interviews