Learning clinical skills during bedside teaching encounters in general practice: a video-observational study with insights from activity theory

Purpose – This paper aims to explore how opportunities for learning clinical skills are negotiated within bedside teaching encounters (BTEs). Bedside teaching, within the medical workplace, is considered essential for helping students develop their clinical skills. Design/methodology/approach – An audio and/or video observational study examining seven general practice BTEs was undertaken. Additionally, audio-recorded, semi-structured interviews were conducted with participants. All data were transcribed. Data analysis comprised Framework Analysis informed by Engeström’s Cultural Historical Activity Theory. Findings – BTEs can be seen to offer many learning opportunities for clinical skills. Learning opportunities are negotiated by the participants in each BTE, with patients, doctors and students playing different roles within and across the BTEs. Tensions emerged within and between nodes and across two activity systems. Research limitations/implications – Negotiation of clinical skills learning opportunities involved shifts in the use of artefacts, roles and rules of participation, which were tacit, dynamic and changing. That learning is constituted in the activity implies that students and teachers cannot be fully prepared for BTEs due to their emergent properties. Engaging doctors, students and patients in refecting on tensions experienced and the factors that infuence judgements in BTEs may be a useful frst step in helping them better manage the roles and responsibilities therein. Originality/value – The paper makes an original contribution to the literature by highlighting the tensions inherent in BTEs and how the negotiation of roles and division of labour whilst juggling two interacting activity systems create or inhibit opportunities for clinical skills learning. This has signifcant implications for how BTEs are conceptualised.

Detection of dynamic background due to swaying movements from motion features

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

AMC: an adaptive multi-level cache algorithm in hybrid storage systems

Hybrid storage systems that consist of flash-based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two-level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two-level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi-level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi-level cache exclusiveness and makes cache resource management more efficient. By using real-life storage traces, our evaluation shows the proposed algorithm improves hybrid multi-level cache performance and also increases the SSD lifetime compared with traditional multi-level cache replacement algorithms.