Soccer science and the Bayes community: exploring the cognitive implications of modern scientific communication

The Benefits, Challenges, and Strategies toward Establishing a Community-Engaged Knowledge Hub: An Integrative Review

Current knowledge creation and mobilization efforts are concentrated in academic institutions. A community-engaged knowledge hub (CEKH) has the potential for transdisciplinary and cross-sectorial collaboration between knowledge producers, mobilizers, and users to develop more relevant and effective research practices as well as to increase community capacity in terms of knowledge production. Objective: To summarize existing original research articles on knowledge hubs or platforms and to identify the benefits, challenges, and ways to address challenges when developing a CEKH. Methods: This study followed a systematic integrative review design. Following a comprehensive search of academic and grey literature databases, we screened 9030 unique articles using predetermined inclusion criteria and identified 20 studies for the final synthesis. We employed thematic analysis to summarize the results. Results: The focus of the majority of these knowledge mobilization hubs was related to health and wellness. Knowledge hubs have a multitude of benefits for the key stakeholders including academics, communities, service providers, and policymakers, including improving dissemination processes, providing more effective community interventions, ensuring informed care, and creating policy assessment tools. Challenges in creating knowledge hubs are generally consistent for all stakeholders, rather than for individual stakeholders, and typically pertain to funding, resources, and conflicting perspectives. As such, strategies to address challenges are also emphasized and should be executed in unison. Conclusions: This study informs the development of a future CEKH through the identification of the benefits, challenges, and strategies to mitigate challenges when developing knowledge hubs. This study addresses a literature gap regarding the comparisons of knowledge hubs and stakeholder experiences.

Rapid learning for precision oncology

The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which the treatment decisions are based. Implementation of Rapid Learning Precision Oncology requires overcoming a host of challenges that include developing analytical tools, capturing the information from each patient encounter and rapidly extrapolating it to other patients, coordinating many patient encounters to efficiently search for effective treatments, and overcoming economic, social and structural impediments, such as obtaining access to, and reimbursement for, investigational drugs.

The promise and perils of pre-publication review: a multi-agent simulation of biomedical discovery under varying levels of review stringency

The Internet has enabled profound changes in the way science is performed, especially in scientific communications. Among the most important of these changes is the possibility of new models for pre-publication review, ranging from the current, relatively strict peer-review model, to entirely unreviewed, instant self-publication. Different models may affect scientific progress by altering both the quality and quantity of papers available to the research community. To test how models affect the community, I used a multi-agent simulation of treatment selection and outcome in a patient population to examine how various levels of pre-publication review might affect the rate of scientific progress. I identified a “sweet spot” between the points of very limited and very strict requirements for pre-publication review. The model also produced a u-shaped curve where very limited review requirement was slightly superior to a moderate level of requirement, but not as large as the aforementioned sweet spot. This unexpected phenomenon appears to result from the community taking longer to discover the correct treatment with more strict pre-publication review. In the parameter regimens I explored, both completely unreviewed and very strictly reviewed scientific communication seems likely to hinder scientific progress. Much more investigation is warranted. Multi-agent simulations can help to shed light on complex questions of scientific communication and exhibit interesting, unexpected behaviors.