Tools for faculty assessment of interdisciplinary competencies of healthcare students: an integrative review

Increasingly, interprofessional teamwork is required for the effective delivery of public health services in primary healthcare settings. Interprofessional competencies should therefore be incorporated within all health and social service education programs. Educational innovation in the development of student-led clinics (SLC) provides a unique opportunity to assess and develop such competencies. However, a suitable assessment tool is needed to appropriately assess student progression and the successful acquisition of competencies. This study adopts an integrative review methodology to locate and review existing tools utilized by teaching faculty in the assessment of interprofessional competencies in pre-licensure healthcare students. A limited number of suitable assessment tools have been reported in the literature, as highlighted by the small number of studies included. Findings identify use of existing scales such as the Interprofessional Socialization and Valuing Scale (ISVS) and the McMaster Ottawa Scale with Team Observed Structured Clinical Encounter (TOSCE) tools plus a range of other approaches, including qualitative interviews and escape rooms. Further research and consensus are needed for the development of teaching and assessment tools appropriate for healthcare students. This is particularly important in the context of interprofessional, community-partnered public health and primary healthcare SLC learning but will be of relevance to health students in a broad range of clinical learning contexts

A Community Platform for Research on Pricing and Distributed Machine Learning

Data generated by increasingly pervasive and intelligent devices has led to an explosion in the use of machine learning (ML) and artificial intelligence, with ever more complex models trained to support applications in fields as diverse as healthcare, finance, and robotics. In order to train these models in a reasonable amount of time, the training is often distributed among multiple machines. However, paying for these machines (either by constructing a local cloud infrastructure or renting machines through an external provider such as Amazon AWS) is very costly. We propose to reduce these costs by creating a marketplace of computing resources designed to support distributed machine learning algorithms. Through our marketplace (coined “DeepMarket”), users can lend their spare computing resources (when not needed) or augment their resources with available DeepMarket machines to train their ML models. Such a marketplace directly provides several benefits for two groups of researchers: (i) ML researchers would be able to train their models with much reduced cost, and (ii) network economics researchers would be able to experiment with different compute pricing mechanisms. The focus of this Demo is to introduce the audience to DeepMarket and its user interface (named “PLUTO”). In particular, we will bring a few laptops with pre-installed PLUTO applications so that users can see how they can create an account on DeepMarket servers, lend their resource, borrow available resources, submit ML jobs, and retrieve the results. Our overall goal is to encourage the conference audience to install PLUTO on their own machines and create a user and developer community around DeepMarket