From 2014-2016, Scholarship of Teaching and Learning (SoTL) Research Fellows at a mid-sized Canadian research-intensive, medical-doctoral university undertook to study their own formation as scholars of teaching and learning, as well as benefits and challenges of their cross-appointment to our central teaching and learning institute from their home academic departments. Findings from surveys and focus groups identified themes such as identity, community, access, transfer, and structural elements (each with benefits and challenges to practice). Our autoethnographic work confirms assertions in the literature about the uneasy relation between SoTL and traditional scholarship, while also bearing out the need for departmental support, and for key interventions along the path from novice to practitioner identity. Some discussion of the ambassador or translator role that can flow from such arrangements is included. De 2014 à 2016, les chercheurs en Avancement des connaissances en enseignement et en apprentissage (ACEA) d’une université canadienne médicale-doctorale de taille moyenne ayant un coefficient de recherche élevé ont entrepris une étude portant sur leur propre formation en tant que chercheurs érudits en matière d’enseignement et d’apprentissage, ainsi que sur les avantages et les défis de leur nomination conjointe à notre institut central d’enseignement et d’apprentissage tout en enseignant dans leur propre département universitaire. Les résultats des sondages et des groupes de discussion ont permis d’identifier certains thèmes tels que l’identité, la communauté, l’accès, le transfert, ainsi que des éléments structuraux (chacun présentant des avantages et des défis concernant la pratique). Notre travail autoethnographique confirme les assertions présentes dans la documentation existante concernant la relation difficile qui existe entre l’ACEA et la recherche traditionnelle, tout en tenant compte de la nécessité du soutien départemental ainsi que pour les interventions clés sur la voie qui consiste à passer de l’identité de novice à celle de praticien. L’article contient également des discussions sur le rôle d’ambassadeur ou de traducteur qui peut découler de tels arrangements.
Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries.
Intelligent systems and applications on manufacturing domain aim to improve decision-making capabilities, ease complex decision problems, offer predictions related to maintenance activities and provide cost savings to companies. In order to support the aforementioned functionalities, the intelligent prediction and decision support systems are based on machine learning and signal processing techniques, AI algorithms, IoT devices, data mining and modeling techniques, rules and fuzzy logic systems, and advance visualizations. In this paper, we introduce an intelligent information management system that aims to provide predictive maintenance and enhance decision support in a leading lift manufacturer. The proposed solution is a decision support system equipped with analytic tools, IoT sensors and visualizations. The system supports the full cycle of polishing procedures of the lift manufacturer, as it starts from predictive maintenance during the polishing machines' operation and ends in the scrap metals' removal after the operation. Both the intelligent information system and the scenario of its usage in the lift manufacturer's shop floor are presented in this work.
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