To address the increasing demand for the use of simulation for assessment, our objective was to review the literature pertaining to simulation-based assessment and develop a set of consensus-based expert-informed recommendations on the use of simulation-based assessment as presented at the 2019 Canadian Association of Emergency Physicians (CAEP) Academic Symposium on Education.

A panel of Emergency Medicine (EM) physicians from across Canada, with leadership roles in simulation and/or assessment, was formed to develop the recommendations. An initial scoping literature review was conducted to extract principles of simulation-based assessment. These principles were refined via thematic analysis, and then used to derive a set of recommendations for the use of simulation-based assessment, organized by the Consensus Framework for Good Assessment. This was reviewed and revised via a national stakeholder survey, and then the recommendations were presented and revised at the consensus conference to generate a final set of recommendations on the use of simulation-based assessment in EM.

We developed a set of recommendations for simulation-based assessment, using consensus-based expert-informed methods, across the domains of validity, reproducibility, feasibility, educational and catalytic effects, acceptability, and programmatic assessment. While the precise role of simulation-based assessment will be a subject of continued debate, we propose that these recommendations be used to assist educators and program leaders as they incorporate simulation-based assessment into their programs of assessment.

In Ontario, Canada, Emergency Medical Care Assistants (EMCAs) have many opportunities for continuing education. However, little is known about how EMCAs learn.

The intent of this study was to explore the distribution of learning styles, preferences for major learning environment characteristics, and the associations between these two factors among the EMCA population in Ontario, Canada.

Following review of the literature, a 32-item survey of learning environment characteristics was constructed to measure the respondents' preferences. Using a random number generator, 386 EMCAs were selected for participation. Each received: a) an explanatory cover letter; b) a copy of the Kolb Learning Style Inventory (LSI) questionnaire; c) a second questionnaire consisting of learning environment characteristics; and d) a stamped, return addressed envelope. Completed surveys were scored to determine the respondent's Learning Style. The LSI and Learning Environment survey results were entered into a data base and subjected to Dual Scaling analysis in order to 1) Identify the distribution of learning styles; and 2) Explore associations between styles and environmental characteristics.

A total of 75 completed surveys were returned, each of the four styles of learning (Converger; Diverger; Assimilator; and Accommodator) were identified in the sample. Dual Scaling analysis indicated a noteworthy association (R(jt) correlation >0.300) between learning style and 10 of the 32 environmental characteristics. The data describe the usefulness of each of the learning styles.

Accommodators believed courses with a strong emphasis on practical applications and working in groups to be very useful, but were less interested in courses with a strong emphasis on theory. Assimilators felt lectures and courses with a strong emphasis on theory very useful, but were less interested in providing input into course objectives. Divergers found that a lot of verbal explanation is useful, but were less interested in working with teachers who act as coaches. Convergers believed that working with teachers who act as coaches is useful. They also preferred courses with a strong emphasis on practical applications, but were less interested in courses with a strong emphasis on theory.

The findings in this study, provide some additional insight into the connections between learning style and elements of the learning environment, and their application may contribute to operationalizing learning theory.