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Background Better use of research evidence (one form of “knowledge”) in health systems requires partnerships between researchers and those who contend with the real-world needs and constraints of health systems. Community-based participatory research (CBPR) and integrated knowledge translation (IKT) are research approaches that emphasize the importance of creating partnerships between researchers and the people for whom the research is ultimately meant to be of use (“knowledge users”). There exist poor understandings of the ways in which these approaches converge and diverge. Better understanding of the similarities and differences between CBPR and IKT will enable researchers to use these approaches appropriately and to leverage best practices and knowledge from each. The co-creation of knowledge conveys promise of significant social impacts, and further understandings of how to engage and involve knowledge users in research are needed. Main text We examine the histories and traditions of CBPR and IKT, as well as their points of convergence and divergence. We critically evaluate the ways in which both have the potential to contribute to the development and integration of knowledge in health systems. As distinct research traditions, the underlying drivers and rationale for CBPR and IKT have similarities and differences across the areas of motivation, social location, and ethics; nevertheless, the practices of CBPR and IKT converge upon a common aim: the co-creation of knowledge that is the result of knowledge user and researcher expertise. We argue that while CBPR and IKT both have the potential to contribute evidence to implementation science and practices for collaborative research, clarity for the purpose of the research—social change or application—is a critical feature in the selection of an appropriate collaborative approach to build knowledge. Conclusion CBPR and IKT bring distinct strengths to a common aim: to foster democratic processes in the co-creation of knowledge. As research approaches, they create opportunities to challenge assumptions about for whom, how, and what is defined as knowledge, and to develop and integrate research findings into health systems. When used appropriately, CBPR and IKT both have the potential to contribute to and advance implementation science about the conduct of collaborative health systems research.

Background Despite increasing interest in research on how to translate knowledge into practice and improve healthcare, the accumulation of scientific knowledge in this field is slow. Few substantial new insights have become available in the last decade. Main body Various problems hinder development in this field. There is a frequent misfit between problems and approaches to implementation, resulting in the use of implementation strategies that do not match with the targeted problems. The proliferation of concepts, theories and frameworks for knowledge transfer – many of which are untested – has not advanced the field. Stakeholder involvement is regarded as crucial for successful knowledge implementation, but many approaches are poorly specified and unvalidated. Despite the apparent decreased appreciation of rigorous designs for effect evaluation, such as randomized trials, these should remain within the portfolio of implementation research. Outcome measures for knowledge implementation tend to be crude, but it is important to integrate patient preferences and the increased precision of knowledge. Conclusions We suggest that the research enterprise be redesigned in several ways to address these problems and enhance scientific progress in the interests of patients and populations. It is crucially important to establish substantial programmes of research on implementation and improvement in healthcare, and better recognize the societal and practical benefits of research.

Background The Promoting Action on Research Implementation in Health Services, or PARIHS framework, was first published in 1998. Since this time, work has been ongoing to further develop, refine and test it. Widely used as an organising or conceptual framework to help both explain and predict why the implementation of evidence into practice is or is not successful, PARIHS was one of the first frameworks to make explicit the multi-dimensional and complex nature of implementation as well as highlighting the central importance of context. Several critiques of the framework have also pointed out its limitations and suggested areas for improvement. Discussion Building on the published critiques and a number of empirical studies, this paper introduces a revised version of the framework, called the integrated or i-PARIHS framework. The theoretical antecedents of the framework are described as well as outlining the revised and new elements, notably, the revision of how evidence is described; how the individual and teams are incorporated; and how context is further delineated. We describe how the framework can be operationalised and draw on case study data to demonstrate the preliminary testing of the face and content validity of the revised framework. Summary This paper is presented for deliberation and discussion within the implementation science community. Responding to a series of critiques and helpful feedback on the utility of the original PARIHS framework, we seek feedback on the proposed improvements to the framework. We believe that the i-PARIHS framework creates a more integrated approach to understand the theoretical complexity from which implementation science draws its propositions and working hypotheses; that the new framework is more coherent and comprehensive and at the same time maintains it intuitive appeal; and that the models of facilitation described enable its more effective operationalisation.

Preclinical researchers confront two overarching agendas related to drug development: selecting interventions amid a vast field of candidates, and producing rigorous evidence of clinical promise for a small number of interventions. We suggest that each challenge is best met by two different, complementary modes of investigation. In the first (exploratory investigation), researchers should aim at generating robust pathophysiological theories of disease. In the second (confirmatory investigation), researchers should aim at demonstrating strong and reproducible treatment effects in relevant animal models. Each mode entails different study designs, confronts different validity threats, and supports different kinds of inferences. Research policies should seek to disentangle the two modes and leverage their complementarity. In particular, policies should discourage the common use of exploratory studies to support confirmatory inferences, promote a greater volume of confirmatory investigation, and customize design and reporting guidelines for each mode.

Despite Canada's investment of hundreds of millions of dollars into researching coronavirus disease 2019 (COVID-19), contributions from other countries have greatly exceeded Canada's research productivity. Additional research funds in Canada have been leveraged during the pandemic, and more may be needed. However, it will take more than just funding to fulfill Canada's health research potential; a culture change is required, along with the will to forge a partnership among the provincial and territorial health systems and the various research institutes and organizations. Here, Lamontagne et al discuss the limitations of the existing clinical research infrastructure in Canada, describe the mechanisms implemented to successfully embed clinical research in the UK health system and provide a roadmap to a Canadian version of the UK system.

[...] conditions are typically caused by many diff erent exposures that might each have moderate eff ects and interact with each other in complex ways.3,4 To investigate a wide range of exposures, extensive information needs to be collected through questionnaires and physical measurements, as well as by storing biological samples that allow many diff erent types of assay (eg, genetic, proteomic, metabonomic, or biochemical). [...] to study reliably the sort of eff ects of diff erent exposures that it is plausible to expect, many thousands of cases of a specifi c disease may be required.4 Prospective cohorts have a number of advantages for the comprehensive and reliable quantifi cation of the combined eff ects of lifestyle, environment, genes, and other exposures on health outcomes.3,5 In particular, exposures can be assessed before they are aff ected by disease or its treatment, or by a person's response to developing disease.

Key Points Crowdsourcing is emerging as a novel framework to tackle scientific problems. A variant of crowdsourcing, scientific competitions known as 'Challenges', enables a rigorous validation of methods, promotes reproducibility and fosters community building. Challenges also accelerate scientific discovery by allowing large numbers of groups to work jointly on a problem. Integrating predictions from different methods submitted by participants to solve a Challenge provides a robust solution that is often better than the best individual solution, a phenomenon known as the 'wisdom of crowds'. The patterns of similar findings that emerge from several independent Challenges can provide useful insight into various key questions in genetics and genomics. Considerable resources are required to gain maximal insights into the diverse big data sets in biomedicine. In this Review, the authors discuss how crowdsourcing, in the form of collaborative competitions (known as Challenges), can engage the scientific community to provide the diverse expertise and methodological approaches that can robustly address some of the most pressing questions in genetics, genomics and biomedical sciences. The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

Analysis shows that funding incentives can work and more funders should trial them, say Pavel V Ovseiko and colleagues

The National Institutes of Health (NIH) invests billions of dollars annually in basic research designed to increase scientific understanding of social, behavioral, biological, and biomedical factors that cause illness, afect recovery, and facilitate health. Because the ultimate goal of this basic research is to improve human health and well-being, translational science, the process by which basic research findings inform prevention and treatment practice, is critical. Two types of translational science are universally identified (although more complex translational frameworks exist1). One is translation of basic science discoveries into new approaches for prevention, diagnosis, or treatment. The other is translation of these new approaches into a form amenable to widespread adoption and implementation. Francis Collins, the current director of the Nil I, has called for a \"comprehensive, systematic, and creative approach to revolutionizing the science of translation. \"4(p1) Here we introduce the multiphase optimization strategy (MOST),5'6 an engineering-inspired framework for development, optimization, and evaluation of multicomponent behavioral, biobehavioral, and biomedical interventions, and show how it offers a novel approach to translational science.