As growth mindset interventions increase in scope and popularity, scientists and policymakers are asking: Are these interventions effective? To answer this question properly, the field needs to understand the meaningful heterogeneity in effects. In the present systematic review and meta-analysis, we focused on two key moderators with adequate data to test: Subsamples expected to benefit most and implementation fidelity. We also specified a process model that can be generative for theory. We included articles published between 2002 (first mindset intervention) through the end of 2020 that reported an effect for a growth mindset intervention, used a randomized design, and featured at least one of the qualifying outcomes. Our search yielded 53 independent samples testing distinct interventions. We reported cumulative effect sizes for multiple outcomes (i.e., mindsets, motivation, behavior, end results), with a focus on three primary end results (i.e., improved academic achievement, mental health, or social functioning). Multilevel metaregression analyses with targeted subsamples and high fidelity for academic achievement yielded, d = 0.14, 95% CI [.06, .22]; for mental health, d = 0.32, 95% CI [.10, .54]. Results highlighted the extensive variation in effects to be expected from future interventions. Namely, 95% prediction intervals for focal effects ranged from -0.08 to 0.35 for academic achievement and from 0.07 to 0.57 for mental health. The literature is too nascent for moderators for social functioning, but average effects are d = 0.36, 95% CI [.03, .68], 95% PI [-.50, 1.22]. We conclude with a discussion of heterogeneity and the limitations of meta-analyses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Growth mindset interventions, which seek to teach the belief that attributes can change, are increasing in popularity and being leveraged to improve health. As these interventions expand in scope, there is a critical need for a systematic review of existing implementation practices to help move the field towards more robust, impactful, and replicable science. To meet this need, we took a three‐fold approach. First, we created a framework for the implementation of mindset interventions (FIMI) using an inductive, interpretive approach that merged existing fidelity frameworks with growth mindset expert opinions. Second, we used the FIMI to frame a systematic, descriptive review of mindset intervention protocols and to create a checklist for transparent reporting of mindset intervention strategies. Third, we provided future research directions that can help to improve the impact of mindset interventions in health‐related contexts.