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The COVID-19 pandemic has resulted in a massive adaptation in health professions education, with a shift from inperson learning activities to a sudden heavy reliance on internet-mediated education. Some health professions schools will have already had considerable educational technology and cultural infrastructure in place, making such a shift more of a different emphasis in provision. For others, this shift will have been a considerable dislocation for both educators and learners in the provision of education. To aid educators make this shift effectively, this 12 Tips article presents a compendium of key principles and practical recommendations that apply to the modalities that make up online learning. The emphasis is on design features that can be rapidly implemented and optimised for the current pandemic. Where applicable, we have pointed out how these short-term shifts can also be beneficial for the long-term integration of educational technology into the organisations' infrastructure. The need for adaptability on the part of educators and learners is an important over-arching theme. By demonstrating these core values of the health professions school in a time of crisis, the manner in which the shift to online learning is carried out sends its own important message to novice health professionals who are in the process of developing their professional identities as learners and as clinicians.

BackgroundSimulation-based research (SBR) is rapidly expanding but the quality of reporting needs improvement. For a reader to critically assess a study, the elements of the study need to be clearly reported. Our objective was to develop reporting guidelines for SBR by creating extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statements.MethodsAn iterative multistep consensus-building process was used on the basis of the recommended steps for developing reporting guidelines. The consensus process involved the following: (1) developing a steering committee, (2) defining the scope of the reporting guidelines, (3) identifying a consensus panel, (4) generating a list of items for discussion via online premeeting survey, (5) conducting a consensus meeting, and (6) drafting reporting guidelines with an explanation and elaboration document.ResultsThe following 11 extensions were recommended for CONSORT: item 1 (title/abstract), item 2 (background), item 5 (interventions), item 6 (outcomes), item 11 (blinding), item 12 (statistical methods), item 15 (baseline data), item 17 (outcomes/ estimation), item 20 (limitations), item 21 (generalizability), and item 25 (funding). The following 10 extensions were recommended for STROBE: item 1 (title/abstract), item 2 (background/rationale), item 7 (variables), item 8 (data sources/measurement), item 12 (statistical methods), item 14 (descriptive data), item 16 (main results), item 19 (limitations), item 21 (generalizability), and item 22 (funding). An elaboration document was created to provide examples and explanation for each extension.ConclusionsWe have developed extensions for the CONSORT and STROBE Statements that can help improve the quality of reporting for SBR (Sim Healthcare 00:00-00, 2016).Electronic supplementary materialThe online version of this article (doi:10.1186/s41077-016-0025-y) contains supplementary material, which is available to authorized users.

As simulation is increasingly used to study questions pertaining to pediatrics, it is important that investigators use rigorous methods to conduct their research. In this article, we discuss several important aspects of conducting simulation-based research in pediatrics. First, we describe, from a pediatric perspective, the 2 main types of simulationbased research: (1) studies that assess the efficacy of simulation as a training methodology and (2) studies where simulation is used as an investigative methodology. We provide a framework to help structure research questions for each type of research and describe illustrative examples of published research in pediatrics using these 2 frameworks. Second, we highlight the benefits of simulation-based research and how these apply to pediatrics. Third, we describe simulation-specific confounding variables that serve as threats to the internal validity of simulation studies and offer strategies to mitigate these confounders. Finally, we discuss the various types of outcome measures available for simulation research and offer a list of validated pediatric assessment tools that can be used in future simulation-based studies.

Change is ubiquitous in health care, making continuous adaptation necessary for clinicians to provide the best possible care to their patients. The authors propose that developing the capabilities of a Master Adaptive Learner will provide future physicians with strategies for learning in the health care environment and for managing change more effectively. The concept of a Master Adaptive Learner describes a metacognitive approach to learning based on self-regulation that can foster the development and use of adaptive expertise in practice. The authors describe a conceptual literature-based model for a Master Adaptive Learner that provides a shared language to facilitate exploration and conversation about both successes and struggles during the learning process.

Learning curves, which graphically show the relationship between learning effort and achievement, are common in published education research but are not often used in day-to-day educational activities. The purpose of this article is to describe the generation and analysis of learning curves and their applicability to health professions education. The authors argue that the time is right for a closer look at using learning curves-given their desirable properties-to inform both self-directed instruction by individuals and education management by instructors.A typical learning curve is made up of a measure of learning (y-axis), a measure of effort (x-axis), and a mathematical linking function. At the individual level, learning curves make manifest a single person's progress towards competence including his/her rate of learning, the inflection point where learning becomes more effortful, and the remaining distance to mastery attainment. At the group level, overlaid learning curves show the full variation of a group of learners' paths through a given learning domain. Specifically, they make overt the difference between time-based and competency-based approaches to instruction. Additionally, instructors can use learning curve information to more accurately target educational resources to those who most require them.The learning curve approach requires a fine-grained collection of data that will not be possible in all educational settings; however, the increased use of an assessment paradigm that explicitly includes effort and its link to individual achievement could result in increased learner engagement and more effective instructional design.

; for the Pediatric Emergency Research Canada (PERC) Concussion Team IMPORTANCE The natural progression of symptom change and recovery remains poorly defined in children after concussion. OBJECTIVES To describe the natural progression of symptom change by age group (5-7, 8-12, and 13-18 years) and sex, as well as to develop centile curves to inform families about children after injury recovery. DESIGN, SETTING, AND PARTICIPANTS Planned secondary analysis of a prospective multicenter cohort study (Predicting Persistent Postconcussive Problems in Pediatrics). The setting was 9 pediatric emergency departments within the Pediatric Emergency Research Canada (PERC) network. Participants were aged 5 to 18 years with acute concussion, enrolled from August 1, 2013, to May 31, 2015, and data analyses were performed between January 2018 and March 2018. EXPOSURES Participants had a concussion consistent with the Zurich Consensus Statement on Concussion in Sport diagnostic criteria and 85% completeness of the Postconcussion Symptom Inventory (PCSI) at each time point. MAIN OUTCOMES AND MEASURES The primary outcome was symptom change, defined as current rating minus preinjury rating (delta score), at presentation and 1, 2, 4, 8, and 12 weeks after injury, measured using the PCSI. Symptoms were self-rated for ages 8 to 18 years and rated by the child and parent for ages 5 to 7 years. The secondary outcome was recovery, defined as no change in symptoms relative to current preinjury PCSI ratings (delta score = 0). Mixed-effects models incorporated the total score, adjusting for random effects (site and participant variability), fixed-effects indicators (age, sex, time, age by time interaction, and sex by time interaction), and variables associated with recovery. Recovery centile curves by age and sex were computed. RESULTS A total of 3063 children (median age, 12.0 years [interquartile range, 9.2-14.6 years]; 60.7% male) completed the primary outcome; 2716 were included in the primary outcome analysis. For the group aged 5 to 7 years, symptom change primarily occurred the first week after injury; by 2 weeks, 75.6% of symptoms had improved (PCSI change between 0 and 2 weeks, −5.3; 95% CI, −5.5 to −5.0). For the groups aged 8 to 12 years and 13 to 18 years, symptom change was prominent the first 2 weeks but flattened between 2 and 4 weeks. By 4 weeks, 83.6% and 86.2% of symptoms, respectively, had improved for the groups aged 8 to 12 years (PCSI change between 0 and 4 weeks, −9.0; 95% CI, −9.6 to −8.4) and 13 to 18 years (PCSI change between 0 and 4 weeks, −28.6; 95% CI, −30.8 to −26.3). Sex by time interaction was significant only for the adolescent group (β = 0.32; 95% CI, 0.21-0.43; P < .001). Most adolescent girls had not recovered by week 12. CONCLUSIONS AND RELEVANCE Symptom improvement primarily occurs in the first 2 weeks after concussion in children and in the first 4 weeks after concussion in preadolescents and male adolescents. Female adolescents appear to have protracted recovery. The derived recovery curves may be usef...

As we capture more and more data about learners, their learning, and the organization of their learning, our ability to identify emerging patterns and to extract meaning grows exponentially. The insights gained from the analyses of these large amounts of data are only helpful to the extent that they can be the basis for positive action such as knowledge discovery, improved capacity for prediction, and anomaly detection. Big Data involves the aggregation and melding of large and heterogeneous datasets while education analytics involves looking for patterns in educational practice or performance in single or aggregate datasets. Although it seems likely that the use of education analytics and Big Data techniques will have a transformative impact on health professional education, there is much yet to be done before they can become part of mainstream health professional education practice. If health professional education is to be accountable for its programs run and are developed, then health professional educators will need to be ready to deal with the complex and compelling dynamics of analytics and Big Data. This article provides an overview of these emerging techniques in the context of health professional education.