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外文期刊>Frontiers in Pediatrics
>Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator
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Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator
Background Frequent simulation-based education is recommended to improve health outcomes during neonatal resuscitation, but is often inaccessible due to time, resource, and personnel requirements. Digital simulation presents a potential alternative, however its effectiveness and reception by healthcare professionals (HCP) remains largely unexplored. Objectives This study explores HCPs’ attitudes towards a digital simulator, technology, and mindset to elucidate their effects on neonatal resuscitation performance in simulation-based assessments. Methods The study was conducted from April-August 2019, with 2-month (June-October 2019) and 5-month (September 2019-January 2020) follow-up at a tertiary perinatal centre in Edmonton, Canada. Of 300 available neonatal HCPs, 50 participated. Participants completed a demographic survey, pre-test, two practice scenarios using the RETAIN neonatal resuscitation digital simulation, post-test, and attitudinal survey (100% response rate). Participants repeated the post-test scenario in two-months (86% response rate) and completed another post-test scenario using a low-fidelity table-top simulator (80% response rate) five-months after the initial study intervention. Participants’ survey responses were collected to measure attitudes towards digital simulation, technology, and mindset. Knowledge was assessed at baseline (pre-test), acquisition (post-test), retention (2-month post-test), and transfer (5-month post-test). Results Fifty neonatal HCPs participated in this study (44 females and 6 males; 27 nurses, 3 nurse practitioners, 14 respiratory therapists, and 6 doctors). Most participants reported technology in medical education as useful and beneficial. Three attitudinal clusters were identified by a hierarchical clustering algorithm based on survey responses. Although participants exhibited diverse attitudinal paths, they all improved neonatal resuscitation performance after using the digital simulator and successfully transferred their knowledge to a new medium. Conclusions Digital simulation improved HCPs’ neonatal resuscitation performance. Medical education may benefit by incorporating technology during simulation training.
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