Observing Interprofessional Collaboration: Impact on Attitude and Knowledge Acquisition

Interprofessional collaboration (IPC) in the medical field is an important factor for good clinical outcomes and should be taught as early as in undergraduate medical education. Since implementing IPC training is an organizational challenge, students are often limited in their opportunities to experience real-life IPC. Therefore, an approach where students observe successful IPC activities of role models in an applied anatomical format was proposed. It was studied whether observing IPC activities in undergraduate anatomical education has an impact on both students' attitude toward IPC and on knowledge acquisition. Further, it was examined whether the attitudes and knowledge of students from different medical disciplines were influenced in different ways. Therefore, 75 medical students and thirty-eight physiotherapy students participated in a study with the task of observing a live broadcast of an interprofessional teaching session. Participants were asked about their attitudes toward interprofessional learning, their evaluation of professional responsibilities, and their profession-specific knowledge before and after observing the IPC session. The participants' attitude toward interprofessional learning improved for both groups of students. Moreover, students of physiotherapy adjusted their evaluation of their own and others' professional responsibilities after observing IPC. In both student groups, knowledge increased, in particular, with respect to the field of knowledge in other professions. So, observing IPC can modify students' attitudes and support knowledge acquisition. The implementation of IPC observations provides students from various healthcare disciplines with a clearer impression of professionals' responsibilities and gives learners the opportunity to acquire knowledge from healthcare fields unfamiliar to them.     

Perfectionism and training distress in junior athletes: a longitudinal investigation

Perfectionistic athletes may train harder and for longer than non-perfectionistic athletes, leaving them susceptible to elevated levels of training distress. So far, however, no study has investigated the relationships between perfectionism and training distress, a key indicator of overtraining syndrome. Furthermore, no study has determined psychological predictors of overtraining syndrome. Using a two-wave design, the present study examined perfectionistic strivings, perfectionistic concerns and training distress in 141 junior athletes (mean age = 17.3 years, range = 16–19 years) over 3 months of active training. Multiple regression analyses were employed to test cross-sectional and longitudinal relationships between perfectionism and training distress. In all analyses, perfectionism emerged as a significant predictor, but strivings and concerns showed differential relationships. When the cross-sectional relationships were regarded, perfectionistic concerns positively predicted training distress (P < .001), whereas perfectionistic strivings negatively predicted training distress (P < .01). When the longitudinal relationships were regarded, only perfectionistic concerns predicted increases in training distress (P < .05), whereas perfectionistic strivings did not (P > .05). The findings suggest that sports scientists who wish to identify athletes at risk of overtraining syndrome may monitor athletes’ perfectionistic concerns as a possible risk factor.     

What goes before the CART? Introducing classification trees with Arbor and CODAP

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build-it-yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.     

The emergence of openness: How and why firms adopt selective revealing in open innovation

Open innovation is often facilitated by strong intellectual property rights (IPRs), but it may also function, and even be boosted, when firms deliberately waive some of their IPRs. Extant literature has pointed out the potential benefits of such behavior, but falls short of explaining what triggers firms to practice it in the first place and to maintain or extend it. Since the waiving of IPRs runs counter to common views on strategy and competition and to engrained practices, this is a non-trivial question. To address it, we conduct an empirical study in a segment of the computer component industry which traditionally has taken a rather proprietary stance. With the advent of the open source operating system Linux, firms increasingly waived their IPRs on software drivers. We trace and analyze this process using both qualitative and quantitative methods. Our results indicate that component makers went through a learning process, which led some to realize how selectively waiving IPRs may be beneficial for their business. We uncover customer demand pull as the initial trigger and observe how a positive feedback loop sets in subsequently, leading to a further increase in the use of selective revealing. Overall, we find that openness develops into a new dimension of competition. We discuss the implication of our findings for research on open innovation and highlight how they impact managers in practice.     

Enhancing scientific discovery learning through metacognitive support

Using a virtual physics lab, we analyzed the impact of metacognitive support on simulation-based scientific discovery learning (SDL). The dependent variables for learning outcome were the immediate conceptual knowledge gain and the retained conceptual knowledge three weeks later. Additional dependent variables were the actual use of a domain-specific cognitive strategy, motivation, emotions, and cognitive load. To contrast the effects of metacognitive support with possible effects of goal specificity, the experimental study followed a 2 × 2 design with a sample of N = 129 ninth grade students and with metacognitive support (yes vs. no) and learning goals (specific vs. nonspecific) as factors. The results showed positive effects of metacognitive support on learning outcome, on actual cognitive strategy use, and on learning emotions. No interaction effect of metacognitive support and goal specificity on learning outcome was observed.