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Misinformation poses significant challenges to evidence-based practice. In the public health domain specifically, treatment misinformation can lead to opportunity costs or direct harm. Alas, attempts to debunk misinformation have proven sub-optimal, and have even been shown to "backfire", including increasing misperceptions. Thus, optimized debunking strategies have been developed to more effectively combat misinformation. The aim of this study was to test these strategies in a real-world setting, targeting misinformation about autism interventions. In the context of professional development training, we randomly assigned participants to an "optimized-debunking" or a "treatment-as-usual" training condition and compared support for non-empirically-supported treatments before, after, and six weeks following completion of online training. Results demonstrated greater benefits of optimized debunking immediately after training; thus, the implemented strategies can serve as a general and flexible debunking template. However, the effect was not sustained at follow-up, highlighting the need for further research into strategies for sustained change.
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