The Unintentional Effects on Body Donation Programs of a Competency-Based Curriculum in Postgraduate Medical Education

As medical programs place increasing importance on competency-based training and surgical simulations for residents, anatomy laboratories, and body donation programs find themselves in a position of adapting to changing demands. To better assess the demand for “life-like” cadaveric specimens and evaluate the possible impacts that competency-based medical education could have upon the body donation program of McGill University, Canada, the authors tracked, over the course of the last 10 years, the number of soft-embalmed specimens, along with the number of teaching sessions and the residents enrolled in competency-based programs that are using cadaveric material. The results reveal that the number of soft-embalmed specimens used within residency training increased from 5 in 2009 to 35 in 2019, representing an increase from 6% of bodies to 36.5% of the total number of body donors embalmed in this institution. Correspondingly, the number of annual teaching sessions for residents increased from 19 in 2012 to 116 in 2019. These increases in teaching are correlated with increasing number of residents enrolled in competency-based programs over the last 3 years (Pearson r ranging from 0.9705 to 0.9903, and R² ranging from 0.9418 to 0.9808). Those results suggest that the new skill-centered curricula which require residents to perform specific tasks within realistic settings, exhibit a growing demand for “life-like” cadaveric specimens. Institutions’ body donation programs must, therefore, adapt to those greater need for cadaveric specimens, which presents many challenges, ranging from the logistical to the ethical.     

Modeling the Pathways to Self-Confidence for Graduate School in Computing

Research in Higher Education - Given the significant need to increase and diversify graduate enrollments within computing fields, it is vital to understand what shapes students’ pathways to...     

Does modality matter? A latent profile and transition analysis of sedentary behaviours among school-aged youth: The UP&DOWN study

ABSTRACT

The aims of this study were to identify profiles of sedentary behaviour (SB) patterns, based on leisure-time self-reported SB modalities (screen, educative, social, and relaxing) and to evaluate changes in these profiles over 2 years among Spanish youth aged 8–18 years. Latent profile analysis (LPA), a data-driven analytic approach, was used to identify groups of boys and girls (n = 1553; 48% girls; mean±SD age: 12.56 ± 2.49 y) with distinct SB profiles using the SB modalities (time/d) as input variables. Latent transition analysis, an extension of LPA that uses longitudinal data, was used to analyse 2-year changes in these profiles. At baseline, four and three SB profiles were found among boys (labelled: screen, educative, social, and relaxing) and girls (labelled: screen/social, educative, and relaxing), respectively. Overall, more girls (range: 48%-67%) had the same profile over time, than boys (40%-52%). Participants with a screen or relaxing SB profile at baseline were more likely to have an educative profile after 2 years. Youth with a social and an educative SB profile at baseline were more likely to transition to profiles characterized by higher screen and social SB, respectively. Using a novel and person-centered approach, this study identified gender-specific SB profiles that were moderately stable over time.     

Deep teaching in a college STEM classroom

Cultural Studies of Science Education - The retention of underrepresented students remains a significant challenge in the STEM (Science, Technology, Engineering and Math) disciplines. A broad range...     

What motivates enrolment in programming MOOCs?

Learners who enrol in massive open online courses (MOOCs) have different backgrounds and tend to have different motivations than learners in traditional courses. Based on value-expectancy theory, an instrument was developed to measure motivation for enrolling in a programming MOOC. A study with 1229 adult participants in Estonian-language programming course “About Programming” was conducted to validate the instrument. Results of confirmatory factor analysis validated the 7-factor scale named factors influencing enrolment in MOOC (FIEM). FIEM comprises three factors of expectancies, three factors of values and one factor of social influence. The highest and lowest rated motivational factors influencing enrolment in programming MOOC are discussed in the paper. Interest in and expectations for the course, personal suitability of distance learning and suitability for family and work are the highest-rated motivational factors for those who enrol in MOOC. Usefulness related to own children, social influence and usefulness to related to certification were the lowest rated. The results of this study can be useful for designers of programming MOOCs and the developed scale might be used in future studies.     

Best of both worlds or refusal to comply? The rich kids of Tehran on Instagram

ABSTRACT

This article offers a textual analysis and semiotic reading of the Rich Kids of Tehran's (RKOT’s) Instagram page. Contributing to scholarship on Iranian youth media practices, this article interrogates how the RKOT navigate urban and rural space to engage in everyday processes of resistance against global and local systemic oppression. Grounding their visual representations on Instagram in historical and cultural context, the author questions how and when quotidian actions are transformed into political transgressions when posted on social media. This article emphasizes the RKOT's agency in shaping their brand by analyzing representations of gender performance, intertextuality, and national identity on Instagram.     

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.