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Introduction: Individual assessment disregards the team aspect of clinical work. Team assessment collapses the individual into the group. Neither is sufficient for medical education, where measures need to attend to the individual while also accounting for interactions with others. Valid and reliable measures of interdependence are critical within medical education given the collaborative manner in which patient care is provided. Medical education currently lacks a consistent approach to measuring the performance between individuals working together as part of larger healthcare team. This review's objective was to identify existing approaches to measuring this interdependence.Methods: Following Arksey & O'Malley's methodology, we conducted a scoping review in 2018 and updated it to 2020. A search strategy involving five databases located >12 000 citations. At least two reviewers independently screened titles and abstracts, screened full texts (n = 161) and performed data extraction on twentyseven included articles. Interviews were also conducted with key informants to check if any literature was missing and assess that our interpretations made sense.Results: Eighteen of the twenty-seven articles were empirical; nine conceptual with an empirical illustration. Eighteen were quantitative; nine used mixed methods. The articles spanned five disciplines and various application contexts, from online learning to sports performance. Only two of the included articles were from the field of Medical Education. The articles conceptualised interdependence of a group, using theoretical constructs such as collaboration synergy; of a network, using constructs such as degree centrality; and of a dyad, using constructs such as synchrony. Both descriptive (eg social network analysis) and inferential (eg multi-level modelling) approaches were described. Conclusion:Efforts to measure interdependence are scarce and scattered across disciplines. Multiple theoretical concepts and inconsistent terminology may be limiting programmatic work. This review motivates the need for further study of measurement techniques, particularly those combining multiple approaches, to capture interdependence in medical education.

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Large-scale software systems handle increasingly larger workloads by implementing highly concurrent and dis tributed design patterns. The thread pool pattern uses pools of pre-existing and reusable threads to limit thread lifecycle over head (thread creation and destruction) and resource thrashing (thread proliferation). However, these advantages are weighed against performance issues caused by concurrency risks, like synchronization errors or deadlock, and thread pool-speclfic risks, like poorly tuned pool size or thread leakage. Detecting these performance issues during load testing requires a thorough understanding of how thread pools behave, yet most performance analysts have limited knowledge of the system and are Hooded with terabytes of data from load tests. We propose a methodology to identify threads with performance deviations in thread pools.Our methodology ranks threads based on the dissimil arity of their resource usage metries. A case study on a large-scale in dustrial software system shows that our methodology can identify threads with performance deviations with an average precision of 100% and an average recall of 76.61%. Our methodology performs very well when ranking long-lived deviations, such as memory leaks, but more work is needed to rank short-lived deviations, such as CPU spikes.Keywords-thread-pools; behaviour-based clustering; under standing ULS systems; I. IN TRODUCTIONThe rise of ultra-large-scale (ULS) e-commerce and telecommunication systems, like Amazon or Facebook, poses a new challenge for the software maintenance field. ULS sys tems require near-perfect up-time and potentially must support hundreds or even thousands of concurrent connections and operations. Failures in such systems are more often associated with an inability to scale to performance demands, than with feature bugs [1], [2]. To ensure that ULS systems are able to perform according to the expected workloads, performance analysts have become key players in the development and maintenance of today's en terprises. Performance analysts are responsible for performing load tests on systems and monitoring how the systems behave under realistic workloads. Such load tests allow performance analysts to determine the maximum operating capacity of a system, to validate non-functional requirements and to uncover bottlenecks. A load test typically lasts from several hours to several days and generates terabytes of performance data and execution logs [3]. Despite the crucial role of performance analysts, current research tends to ignore their needs. The current state of the practice requires significant manual review of the performance data and execution logs and a high degree of insight into the 978-1-4577-0664-6/1lI$26.00 ©2011 IEEE 83 system behaviour [2], [4], [5]. Tool support is lacking because most research techniques require either heavy instrumentation (disrupting the work-load) or more detailed log entries [2], [5]-[8] (often unavailable during load tests because collecting detailed logs impacts the system's performance...