Jeni L Burnette, Joseph Billingsley, George C Banks, Laura E Knouse, Crystal L Hoyt, Jeffrey M Pollack, Stefanie Simon
{"title":"A systematic review and meta-analysis of growth mindset interventions: For whom, how, and why might such interventions work?","authors":"Jeni L Burnette, Joseph Billingsley, George C Banks, Laura E Knouse, Crystal L Hoyt, Jeffrey M Pollack, Stefanie Simon","doi":"10.1037/bul0000368","DOIUrl":null,"url":null,"abstract":"<p><p>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, <i>d</i> = 0.14, 95% CI [.06, .22]; for mental health, <i>d</i> = 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 <i>d</i> = 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).</p>","PeriodicalId":17,"journal":{"name":"ACS Infectious Diseases","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Infectious Diseases","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/bul0000368","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/10/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 29
Abstract
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).
期刊介绍:
ACS Infectious Diseases will be the first journal to highlight chemistry and its role in this multidisciplinary and collaborative research area. The journal will cover a diverse array of topics including, but not limited to:
* Discovery and development of new antimicrobial agents — identified through target- or phenotypic-based approaches as well as compounds that induce synergy with antimicrobials.
* Characterization and validation of drug target or pathways — use of single target and genome-wide knockdown and knockouts, biochemical studies, structural biology, new technologies to facilitate characterization and prioritization of potential drug targets.
* Mechanism of drug resistance — fundamental research that advances our understanding of resistance; strategies to prevent resistance.
* Mechanisms of action — use of genetic, metabolomic, and activity- and affinity-based protein profiling to elucidate the mechanism of action of clinical and experimental antimicrobial agents.
* Host-pathogen interactions — tools for studying host-pathogen interactions, cellular biochemistry of hosts and pathogens, and molecular interactions of pathogens with host microbiota.
* Small molecule vaccine adjuvants for infectious disease.
* Viral and bacterial biochemistry and molecular biology.