Barriers to Citizen Science and Dissemination of Knowledge in Healthcare

Abstract

Citizen science goes by many names, especially when citizen science is applied to healthcare and biomedicine: There are patient researchers, patient-driven innovators, lead users, quantified self-trackers, and more. Citizen science in healthcare goes beyond data collection; citizen scientists themselves perform many stages of science and research all the way through to dissemination of their work. They may find themselves facing barriers similar to those facing traditional academic researchers in academic publishing; however, these challenges are exacerbated by the very nature of citizen science, which is often individually driven and unfunded. Additional barriers in dissemination, such as structural barriers of journal publication systems, conscious and subconscious biases of editors and reviewers, the financial and time costs associated with dissemination, and more, can challenge citizen scientists. Some of the barriers are concrete while others are perceived but may be no less challenging. This essay highlights the barriers of citizen science work in healthcare, with special attention to the challenges of dissemination, including those experienced by a citizen scientist who has been addressing these challenges for nearly a decade. I provide three potential strategies with relevant examples for addressing some of the dissemination challenges, and provide additional dissemination advice-and encouragement-for citizen scientists in biomedicine and healthcare.

Citizen Science for Health: an international survey on its characteristics and enabling factors

“…In their work, they identify a variety of ethical issues and considerations which are overdue for conversation and implementation (Wiggins & Wilbanks, 2019). Similar barriers to citizen science and patient-led research based in non-adapted ethical review procedures have been identified in other works as well (Lewis, 2022, Fiske et al, 2019. As such, the question of how to design and implement ethical oversight methodologies that can help facilitate health-related citizen sciencewhile serving and protecting non-institutional participantsremains an open and urgent question that should be given attention to help grow health-related citizen science.…”

“…We see some evidence of this in our survey, as people with health conditions on average do rank access to literature higher than academic researchers (Figure 6). Beyond barriers to access in terms of reading the literature, and even though that some publishers are currently promoting the inclusion of patients as co-authors and also involved in the peer-review process in journals such as BMC Medicine or the Lancet (Boyce 2018), patients face an even higher barrier when it comes to trying to publish their own original research (Lewis 2022). This is reflected in the other condition for growth of citizen science practice in the health domain, 'citizen visibility'.…”

Exploring public health researchers’ approaches, barriers, and needs regarding dissemination: A mixed-methods exploration

“…Planned strategies such as through news media, social media, policy briefs, one-on-one meetings, workshops, and seminars [ 7 ] are better suited to reach an audience that can create societal change, e.g., practitioners and policymakers [ 1 ]. Importantly, the use of traditional dissemination methods is linked to significant costs, including submission fees, article publishing charges (APCs), or open access (OA) charges [ 21 ]. Brownson and colleagues [ 10 ] plea for a change in how research is funded and how researchers are incentivized, requiring institutions to commit to dissemination for the long term.…”

Development of a Dynamic Network Model to Identify Temporal Patterns of Structural Malformations in Zebrafish Embryos Exposed to a Model Toxicant, Tris(4-chlorophenyl)methanol

“…These matrix representations are developed as follows. The formal mathematical graph representation of the network, as described in [25], is G(t) = {N(t), L(t), f (t)}, with time t, nodes N, links L, and mapping function f : N × N that connects node pairs for network topology. The proposed network model contains K = 6 nodes and M = 15 weighted links.…”