Investigating the Extent Realistic Moulage Impacts on Immersion and Performance Among Undergraduate Paramedicine Students in a Simulation-based Trauma Scenario: A Pilot Study.

INTRODUCTION: Many healthcare education commentators suggest that moulage can be used in simulation to enhance scenario realism. However, few studies investigate to what extent using moulage in simulation impacts learners. We undertook a mixed-methods pilot study investigating how moulage influences student immersion and performance in simulation. METHODS: Fifty undergraduate paramedicine students were randomized into two groups completing a trauma-based scenario with or without patient moulage. Task immersion was determined via a self-report questionnaire (National Aeronautics and Space Administration Task Load Index), eye-tracking, and postsimulation interviews. Performance was measured via independent observation of video by two paramedic clinical educators and time-to-action-when students first applied pressure to the primary wound. RESULTS: Eye-tracking suggested that students attended to the thigh wound more often with the inclusion of moulage than without. National Aeronautics and Space Administration Task Load Index data suggested that the inclusion of moulage heightened students' feeling of being rushed throughout the scenario. This elicited an expedited performance of tasks with moulage present compared with not. Students experienced greater immersion with the inclusion of moulage. However, including moulage enhanced scenario difficulty to the extent that overall clinical performance was negatively affected. However, no differences were found when more heavily weighting items felt to contribute most to the survivability of the patient. CONCLUSIONS: Including moulage engendered immersion and a greater sense of urgency and did not sacrifice performance of key life-saving interventions. As a result of undertaking this pilot project, we suggest that a large-scale randomized controlled trial is feasible and should be undertaken before implementing change to curricula.

Genotype phenotype mapping in RNA viruses - disjunctive normal form learning.

RNA virus phenotypic changes often result from multiple alternative molecular mechanisms, where each mechanism involves changes to a small number of key residues. Accordingly, we propose to learn genotype-phenotype functions, using Disjunctive Normal Form (DNF) as the assumed functional form. In this study we develop DNF learning algorithms that attempt to construct predictors as Boolean combinations of covariates. We demonstrate the learning algorithm's consistency and efficiency on simulated sequences, and establish their biological relevance using a variety of real RNA virus datasets representing different viral phenotypes, including drug resistance, antigenicity, and pathogenicity. We compare our algorithms with previously published machine learning algorithms in terms of prediction quality: leave-one-out performance shows superior accuracy to other machine learning algorithms on the HIV drug resistance dataset and the UCIs promoter gene dataset. The algorithms are powerful in inferring the genotype-phenotype mapping from a moderate number of labeled sequences, as are typically produced in mutagenesis experiments. They can also greedily learn DNFs from large datasets. The Java implementation of our algorithms will be made publicly available.