Determining how to communicate complex research findings can pose a significant challenge for researchers. “We must consider not only where and to whom we disseminate findings, but also how to craft clear messages using language that is understandable, yet statistically accurate and true to the study design,” says Janet Prvu Bettger, ScD.

Bettger’s research team conducted a comparative effectiveness study to examine which types of services offer the best options for rehabilitation and recovery for acute stroke patients following hospitalization. The study design involved multiple complex analyses of large datasets looking at several different subgroups and outcomes across multiple time points.

Bettger found that in developing manuscripts for publication, the task of framing succinct conclusions was especially challenging, given there was significant variation among findings for different subgroups and outcomes. These nuances, and the complexity of the analytic methods, made it harder for peer reviewers and journal editors to assess the contribution of the research.

Bettger also stresses the importance of tailoring messages for different audiences. In addition to peer-reviewed journal publications, which are geared toward more technical and clinical audiences, communicating research findings to lay audiences such as patients and caregivers requires that “findings be presented in easily digestible terms, with conclusions that are easy to comprehend,” she says.

However, simplifying complex findings can be problematic. Stating conclusions too broadly can be misleading or overstate results, while being too generic can “water down” research findings. Researchers should give careful consideration to the language they use to position findings when communicating results to nontechnical audiences, suggests Bettger. For example, causation and whether there was benefit or harm for a treatment or intervention can only be determined from a randomized clinical trial. The language to describe positive findings from an observational study can be difficult for the general public to comprehend. Likelihood, chance, probability, risk, and the strength of an association may need more of an explanation for readers to understand how to apply the findings.

Even when research teams are careful to clearly and accurately communicate study methods and results, sometimes secondary users may still generalize key messages to include “causal” language. Writing even technical reports for a general audience could ensure more appropriate use of research findings.