Reaching out: medical students leading in local communities

Background: Queen's University Red Cross is a medical student-led volunteer group with a key aim of promoting social change within local communities and empowering young people to aspire to higher education. We describe ‘The Personal Development Certificate’, a 12–week community development programme devised by third-year medical students at Queen's University Belfast to target young people who are lacking educational motivation, are disengaged at home or are marginalised through social circumstances.

Context: Community-based education is of increasing importance within undergraduate and postgraduate medical education in the UK, and further afield. We evaluated the perceived improvements in key skills such as teamwork, leadership, communication, and problem solving in students following participation in this programme, and the extent to which their attitude and appreciation of community-based medicine changed.

Innovation: Following facilitation of this community-based initiative, all students reported a perceived improvement in the acquired skill sets. Students made strong links from this programme to previous clinical experiences and appreciated the opportunity to translate a series of classroom-learned skills to real-life environments and interactions. The students’ appreciation and understanding of community-based medicine was the single most improved area of our evaluation.

Implications: We have demonstrated that medical students possess the skills to develop and facilitate their own educational projects. Non-clinical, student-led community projects have the potential to be reproduced using recognised frameworks and guidelines to complement the current undergraduate medical curriculum

Empowering Human Services Organizations to Embrace Evidence-informed Practice: International Best Practices

Human service organizations are increasingly using knowledge as a mechanism for implementing change. Knowledge emerging from many sources that may include academic publications, grey literature, and service user and practitioner wisdom contributes toward informing best practice. The question is: how do we harness this knowledge to make practice more effective? This paper synthesizes the lessons learned from eight international organizations that have made a commitment to knowledge mobilization as an important priority in their mission and operation. The paper provides a conceptual model, tools and resources to help human services organizations create strategies for building, enhancing or sustaining their knowledge mobilization efforts. The paper describes a flexible blueprint for human service organizations to leverage knowledge mobilization efforts at all levels of service delivery.

Criminal Justice Reforms in Northern Ireland: The Agents of Change

This chapter examines who and what brought about the transformation in the criminal justice system of Northern Ireland between 1998 and 2015, seeking to pinpoint the critical moments which stimulated the reforms, how they were delivered, and through what processes they are now being maintained. It seeks to identify the key agents of change and considers whether it is possible to generalise from Northern Ireland’s experience so that other conflicted societies might benefit from the lessons learned.

Supervisor Continuity or Co-Location: Which Matters in Residency Education? Findings From a Qualitative Study of Remote Supervisor Family Physicians in Australia and Canada

Purpose: Changes to health care systems andworking hours have fragmentedresidents’ clinical experiences withpotentially negative effects ontheir development as professionals.Investigation of off-site supervision,which has been implemented in isolatedrural practice, could reveal importantbut less overt components of residencyeducation. 

Method: Insights from sociocultural learningtheory and work-based learning provideda theoretical framework. In 2011–2012,16 family physicians in Australia andCanada were asked in-depth how theyremotely supervised residents’ workand learning, and for their reflectionson this experience. The verbatiminterview transcripts and researchers’memos formed the data set. Templateanalysis produced a description andinterpretation of remote supervision. 

Results: Thirteen Australian family physiciansfrom five states and one territory, andthree Canadians from one province,participated. The main themes werehow remoteness changed the dynamicsof care and supervision; the importanceof ongoing, holistic, nonhierarchical,supportive supervisory relationships; andthat residents learned “clinical courage”through responsibility for patients’ careover time. Distance required supervisorsto articulate and pass on their expertiseto residents but made monitoringdifficult. Supervisory continuityencouraged residents to build on pastexperiences and confront deficiencies. 

Conclusions: Remote supervision enabled residents todevelop as clinicians and professionals.This questions the supremacy of co-locationas an organizing principle forresidency education. Future specialists maybenefit from programs that give themongoing and increasing responsibilityfor a group of patients and supportive.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.