Recruitment and retention of tutors in problem-based learning: why teachers in medical education tutor

Introduction: Problem-based learning (PBL) is resource-intensive, particularly as it relates to tutors for small group learning. This study explores the factors that contributed to tutor participation in PBL in a medical training program, examining tutor recruitment and retention within the larger scope of teacher satisfaction and motivation in higher education. Method: From 2007 to 2010, following the introduction of new PBL-based curriculum in undergraduate medical education, all faculty members serving as tutors were invited to attend an interview as part of this study. Semi-structured interviews approximately one hour in length were conducted with 14 individuals- 11 who had tutored in PBL within the Faculty of Medicine and Dentistry and 3 faculty members who had chosen not to participate in PBL. Thematic analysis was employed as the framework for analysis of the data. Results: Seven factors were identified as affecting recruitment and retention of tutors in the undergraduate medical education program. Discussion: We suggest that identification and strengthening of the factors that promote tutor recruitment and retention may serve to strengthen PBL initiatives and, furthermore, may increase our understanding of motivation by academics in other aspects of medical education

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Predicting species distributions for conservation decisions

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes