Introduction to PRISMA 2020 and implications for research synthesis methodologists

Page, Matthew J.; Moher, David; McKenzie, Joanne E.

doi:10.1002/jrsm.1535

Cited by 136 publications

(86 citation statements)

References 68 publications

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“…The study protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO; http://www.crd.york.ac.th/PROSPERO, protocol number: CRD42021284759). This report follows the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Statement (PRISMA) 2020 30 . The PRISMA 2020 checklist for this review was provided in the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta‐analysis of longitudinal studies

et al. 2022

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Summary This study examined the predictability of child weight status on executive function (EF) and vice versa. We searched PubMed, CINAHL, Web of Science, and EMBASE for longitudinal studies conducted in children and adolescents on October 31, 2021. A pairwise meta‐analysis was performed using a frequentist random‐effects approach. The quality of all included studies was evaluated using Newcastle–Ottawa Scale and GRADE assessments. This study included 18 longitudinal studies (N = 30,101). Overall executive functioning was a significant negative predictor of child weight status (pooled beta coefficient = −0.14; 95% confidence interval [CI] [−0.22 to −0.07]; I2 = 97%). The pooled odds ratio also revealed that high EF children had a significant lower risk for developing overweight/obesity (odds ratio [OR] = 0.72; 95% CI [0.59 to 0.87]; I2 = 72%). Conversely, child weight status was a significant negative predictor of overall executive functioning (pooled beta coefficient = −0.06; 95% CI [−0.12 to −0.01]; I2 = 81%). These results suggest a bidirectional prediction between child weight status and EF. These predictabilities are low but potentially beneficial for implementation in childcare systems.

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Section: Methodsmentioning

confidence: 99%

Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta‐analysis of longitudinal studies

et al. 2022

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“…This review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (36) and the PRISMA-S extension (37). The protocol for this review was developed by the authors and registered with the PROSPERO International Prospective Register of Systematic Reviews (CRD42021253547).…”

Section: Search Strategy and Selection Criteriamentioning

confidence: 99%

Post-secondary Student Mental Health During COVID-19: A Meta-Analysis

et al. 2021

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The COVID-19 pandemic has posed notable challenges to post-secondary students, causing concern for their psychological well-being. In the face of school closures, academic disruptions, and constraints on social gatherings, it is crucial to understand the extent to which mental health among post-secondary students has been impacted in order to inform support implementation for this population. The present meta-analysis examines the global prevalence of clinically significant depression and anxiety among post-secondary students during the COVID-19 pandemic. Several moderator analyses were also performed to examine sources of variability in depression and anxiety prevalence rates. A systematic search was conducted across six databases on May 3, 2021, yielding a total of 176 studies (1,732,456 participants) which met inclusion criteria. Random-effects meta-analyses of 126 studies assessing depression symptoms and 144 studies assessing anxiety symptoms were conducted. The pooled prevalence estimates of clinically elevated depressive and anxiety symptoms for post-secondary students during the COVID-19 pandemic was 30.6% (95% CI: 0.274, 0.340) and 28.2% (CI: 0.246, 0.321), respectively. The month of data collection and geographical region were determined to be significant moderators. However, student age, sex, type (i.e., healthcare student vs. non-healthcare student), and level of training (i.e., undergraduate, university or college generally; graduate, medical, post-doctorate, fellow, trainee), were not sources of variability in pooled rates of depression and anxiety symptoms during the pandemic. The current study indicates a call for continued access to mental health services to ensure post-secondary students receive adequate support during and after the COVID-19 pandemic.Systematic Review Registration: PROSPERO website: https://www.crd.york.ac.uk/prospero/, identifier: CRD42021253547.

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“…To ensure a systematic review process, we adopt the approach used in the PRISMA guidelines [60]. Figure 2 illustrates the steps utilized in our review process.…”

Section: Methodsmentioning

confidence: 99%

A Systematic Review of Deep Knowledge Graph-Based Recommender Systems, with Focus on Explainable Embeddings

Doh

Zhou

Arthur

et al. 2022

Data

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Recommender systems (RS) have been developed to make personalized suggestions and enrich users’ preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as black boxes, not presenting the user with insights into the system logic or reasons for recommendations. Recently, generating explainable recommendations with deep knowledge graphs (DKG) has attracted significant attention. DKG is a subset of explainable artificial intelligence (XAI) that utilizes the strengths of deep learning (DL) algorithms to learn, provide high-quality predictions, and complement the weaknesses of knowledge graphs (KGs) in the explainability of recommendations. DKG-based models can provide more meaningful, insightful, and trustworthy justifications for recommended items and alleviate the information explosion problems. Although several studies have been carried out on RS, only a few papers have been published on DKG-based methodologies, and a review in this new research direction is still insufficiently explored. To fill this literature gap, this paper uses a systematic literature review framework to survey the recently published papers from 2018 to 2022 in the landscape of DKG and XAI. We analyze how the methods produced in these papers extract essential information from graph-based representations to improve recommendations’ accuracy, explainability, and reliability. From the perspective of the leveraged knowledge-graph related information and how the knowledge-graph or path embeddings are learned and integrated with the DL methods, we carefully select and classify these published works into four main categories: the Two-stage explainable learning methods, the Joint-stage explainable learning methods, the Path-embedding explainable learning methods, and the Propagation explainable learning methods. We further summarize these works according to the characteristics of the approaches and the recommendation scenarios to facilitate the ease of checking the literature. We finally conclude by discussing some open challenges left for future research in this vibrant field.

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Introduction to PRISMA 2020 and implications for research synthesis methodologists

Cited by 136 publications

References 68 publications

Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta‐analysis of longitudinal studies

Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta‐analysis of longitudinal studies

Post-secondary Student Mental Health During COVID-19: A Meta-Analysis

A Systematic Review of Deep Knowledge Graph-Based Recommender Systems, with Focus on Explainable Embeddings

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