Sustainability of evidence-based healthcare: Research agenda, methodological advances, and infrastructure support

BACKGROUND: Little is known about how well or under what conditions health innovations are sustained and their gains maintained once they are put into practice. Implementation science typically focuses on uptake by early adopters of one healthcare innovation at a time. The later-stage challenges of scaling up and sustaining evidence-supported interventions receive too little attention. This project identifies the challenges associated with sustainability research and generates recommendations for accelerating and strengthening this work. METHODS: A multi-method, multi-stage approach, was used: (1) identifying and recruiting experts in sustainability as participants, (2) conducting research on sustainability using concept mapping, (3) action planning during an intensive working conference of sustainability experts to expand the concept mapping quantitative results, and (4) consolidating results into a set of recommendations for research, methodological advances, and infrastructure building to advance understanding of sustainability. Participants comprised researchers, funders, and leaders in health, mental health, and public health with shared interest in the sustainability of evidence-based health care. RESULTS: Prompted to identify important issues for sustainability research, participants generated 91 distinct statements, for which a concept mapping process produced 11 conceptually distinct clusters. During the conference, participants built upon the concept mapping clusters to generate recommendations for sustainability research. The recommendations fell into three domains: (1) pursue high priority research questions as a unified agenda on sustainability; (2) advance methods for sustainability research; (3) advance infrastructure to support sustainability research. CONCLUSIONS: Implementation science needs to pursue later-stage translation research questions required for population impact. Priorities include conceptual consistency and operational clarity for measuring sustainability, developing evidence about the value of sustaining interventions over time, identifying correlates of sustainability along with strategies for sustaining evidence-supported interventions, advancing the theoretical base and research designs for sustainability research, and advancing the workforce capacity, research culture, and funding mechanisms for this important work

A Research-Based Model for Digital Mapping and Art History: Notes from the Field

Most digital mapping in art history today divides the research process from the visualization aspects of the project. This problem became the focus of a summer institute that Paul Jaskot and Anne Kelly Knowles ran at Middlebury College with the support of the Samuel H. Kress Foundation. Our article both reports on the institute and suggests how research questions can complement digital mapping methods. We conclude with three case studies of spatial questions in art history and discuss the Fellows’ use of GIS to explore examples from Qing Dynasty China, medieval Gotland, and contemporary New York City

Mixed Methods Research in LIS Literature: A Scoping Review

This poster presentation introduces the initial stages of a scoping review, mapping the existing mixed methods research (MMR) studies in the field of Library and Information Science (LIS) published between 2013-2015. While MMR has no universal definition, it can generally be defined as a mixing of qualitative and quantitative research methods or analytic techniques in a single study (Creswell, 2010). Mixed methods offers a third research paradigm, which aligns more closely with the pragmatic approach being adopted by research which focuses on the research question driving the methods, and not the inclinations of the researcher (Onwuegbuzie & Leech, 2005; Small, 2011; Yoshikawa, Weisner, Kalil & Way, 2008). In order to expand research methods to best answer LIS research questions, a better understanding of MMR becomes critical for its useful implementation. The research questions guiding this scoping review are: How is MMR being used in LIS research? What types of qualitative and quantitative methods are being used within a MMR study? How are researchers defining and classifying these studies in relation to the most prominent definitions within the MMR community? What types of research questions are being addressed through MMR? This scoping review will allow academic and research librarians to see how their colleagues are implementing MMR across the field in the intention that they will have a better understanding of mixed methods approaches and feel more confident in using them. The poster presentation will report on the search strategy and initial findings of this scoping review

Explainable artificial intelligence for omics data: a systematic mapping study

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies’ AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners

Workshop: Lassoing unicorns: how to map capabilities for better interdisciplinary research

Sometimes doing interdisciplinary work feels like trying to lasso unicorns. Working with big players from foreign disciplines and interdepartmental drifters. Negotiating over language and frameworks. Agreeing common research questions. All the while trying to gather data and do good work. Building on methods developed in ESRC and Horizon 2020 funded projects on interdisciplinary research, this workshop will: a) introduce the concept interdisciplinary capabilities - the disciplinary skills and informal aptitudes needed for people like environmental engineers, ecological economists and machine learning developers to work well together. b) present a mixed-method approach to mapping capabilities using bibliometric analysis and interviews. c) offer hands-on reflexive exercises on personal ‘capability mapping’, tailored to workshop participants The session will help scholars of all levels recognise power and knowledge in research and identify opportunities to steer that research together

Paper2GIS: Going postal in the midst of a pandemic

It is widely agreed that using local knowledge and opinions can prove beneficial in the decision-making process, with various forms of Participatory Mapping being used to capture responses to spatial questions. However, remote participatory research is increasingly carried out using digital methods which can limit the involvement of those affected by digital divides. This research uses a novel, automatic self-digitising paper-based Participatory Mapping method to explore whether the accessibility needs of participants can be met whilst maintaining the potential for effective spatial analysis on the part of the researcher. As a paper-based, geographically specific approach this research could be conducted during the pandemic by post, with residents of the Outer Hebrides, UK

Leadership and Corporate Social Responsibility : Mapping the Conceptual Structure of Research

Purpose: The aim of the study is to identify, with the use of co-word analysis, and explore leading thematic areas in research investigating intersections between leadership and corporate social responsibility. The research process is focused around the following study questions: (1) What are the leading thematic areas in research on intersections between leadership and corporate social responsibility?, (2) Are there similarities and differences between leading topics identified through co-word analysis with the findings from previous studies employing systematic literature review and research topic profiling methodologies? (3) What is the usefulness of co-word analysis for mapping leading thematic areas comparing to systematic literature reviews and research topic profiling?Design/methodology/approach: The study employs the method of keywords co-occurrence analysis, which is a type of co-word analysis, categorized among science mapping methods. The bibliometric data for analysis are retrieved from Scopus. The analysis and visualization of keywords co-occurrence is supported by VOSviewer software.Findings: The four following thematic areas have been identified in research production on intersections between leadership and corporate social responsibility: (1) sustainable strategy and sustainable development, (2) leadership and ethics, (3) humans in the organisationalcontext, and (4) responsible leaders in sustainable and responsible organisations. Thematic areas identified through co-word analysis show similarities with the findings from previous mapping studies conducted with the use of systematic literature review and research topic profiling. More similarity is visible in the case of research topic profiling, which likewise co-word analysis is categorized among bibliometric methods. As a method used for mapping thematic structure of a research field, co-word analysis is found to show more advantages than research topic profiling and systematic literature reviews.Research and methodological implications: The study contributes to the body of knowledge through identifying leading thematic areas in research on intersections between leadership and corporate social responsibility. It is also valuable from the methodological point of view as it gives the chance to discuss advantages and disadvantages of various research methods employed for mapping the conceptual structure of a research field.Originality/value: The added value of the study is mostly of theoretical and methodological character. The study seems to be the first attempt to map the conceptual structure of the analysed research field with the use of keywords co-occurrence analysis. It also compares and contrasts the findings from various methods used for mapping the conceptual structure of the field.Paper type: Review

Measuring Software Process: A Systematic Mapping Study

Context: Measurement is essential to reach predictable performance and high capability processes. It provides support for better understanding, evaluation, management, and control of the development process and project, as well as the resulting product. It also enables organizations to improve and predict its process’s performance, which places organizations in better positions to make appropriate decisions. Objective: This study aims to understand the measurement of the software development process, to identify studies, create a classification scheme based on the identified studies, and then to map such studies into the scheme to answer the research questions. Method: Systematic mapping is the selected research methodology for this study. Results: A total of 462 studies are included and classified into four topics with respect to their focus and into three groups based on the publishing date. Five abstractions and 64 attributes were identified, 25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the most measured process attributes, while effort and performance were the most measured project attributes. Goal Question Metric and Capability Maturity Model Integration were the main methods and models used in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently identified research contexts.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2- RMinisterio de Economía y Competitividad TIN2015-71938-RED

Ten Simple Rules for Responsible Big Data Research

The use of big data research methods has grown tremendously over the past five years in both academia and industry. As the size and complexity of available datasets has grown, so too have the ethical questions raised by big data research. These questions become increasingly urgent as data and research agendas move well beyond those typical of the computational and natural sciences, to more directly address sensitive aspects of human behavior, interaction, and health. The tools of big data research are increasingly woven into our daily lives, including mining digital medical records for scientific and economic insights, mapping relationships via social media, capturing individuals’ speech and action via sensors, tracking movement across space, shaping police and security policy via “predictive policing,” and much more