Shuai Ma, Zhihong Xu, Peng Lu, Jean Parrella, Ashlynn Kogut
This is the protocol for a Campbell systematic review. The objectives are as follows. Our review will exclusively emphasize quantitative evidence from experimental studies, identifying the important factors and providing comprehensive and in-depth recommendations, with a primary focus on identifying the effective communication and marketing strategies that have been evaluated. The findings of the study focusing on consumers' acceptance will provide valuable insight for policymakers to combat the food waste issue. The research questions are as follows: RQ1: What are the key factors influencing consumer acceptance in experimental studies on upcycled foods? RQ2: What communication and marketing strategies have been used to increase consumer acceptance in experimental studies on upcycled foods?
{"title":"Protocol: The Effects of Communication Strategies on Upcycled Food Acceptance: A Systematic Review and Meta-Analysis","authors":"Shuai Ma, Zhihong Xu, Peng Lu, Jean Parrella, Ashlynn Kogut","doi":"10.1002/cl2.70075","DOIUrl":"https://doi.org/10.1002/cl2.70075","url":null,"abstract":"<p>This is the protocol for a Campbell systematic review. The objectives are as follows. Our review will exclusively emphasize quantitative evidence from experimental studies, identifying the important factors and providing comprehensive and in-depth recommendations, with a primary focus on identifying the effective communication and marketing strategies that have been evaluated. The findings of the study focusing on consumers' acceptance will provide valuable insight for policymakers to combat the food waste issue. The research questions are as follows: RQ1: What are the key factors influencing consumer acceptance in experimental studies on upcycled foods? RQ2: What communication and marketing strategies have been used to increase consumer acceptance in experimental studies on upcycled foods?</p>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cl2.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ella Flemyng, Anna Noel-Storr, Biljana Macura, Gerald Gartlehner, James Thomas, Joerg J. Meerpohl, Zoe Jordan, Jan Minx, Angelika Eisele-Metzger, Candyce Hamel, Paweł Jemioło, Kylie Porritt, Matthew Grainger
<p>Evidence syntheses, including systematic reviews, are a type of research that uses systematic, replicable methods to evaluate all available evidence on a specific question. They are built on the principles of research integrity, including rigor, transparency, and reproducibility. There is wide recognition that artificial intelligence (AI) and automation have the potential to transform the way we produce evidence syntheses, making the process significantly more efficient. However, this technology is potentially disruptive, characterized by opaque decision-making and black-box predictions, susceptible to overfitting, potentially embedded with algorithmic biases, and at risk of fabricated outputs and hallucinations. To safeguard evidence synthesis as the cornerstone of trusted, evidence-informed decision making, Cochrane, the Campbell Collaboration, JBI and the Collaboration for Environmental Evidence (CEE), have come together to collaborate on a responsible and pragmatic approach to AI use in evidence synthesis.</p><p>By AI, we mean different types of automation, as described within the Responsible use of AI in evidence SynthEsis recommendations (RAISE) (Thomas et al. <span>2025a</span>), specifically, “advanced technologies that enable machines to do highly complex tasks effectively – which would require intelligence if a person were to perform them.” This ranges from general automation applications, such as rule-based or trained machine learning algorithms, to more recent large language models and generative AI approaches.</p><p>Incorporating AI in evidence synthesis comes with challenges as well as opportunities. While it is clear we need to make better use of AI for evidence synthesis to become more timely, affordable, and sustainable, we must also acknowledge the environmental and social costs associated with some forms of AI, particularly large-scale language models. There are risks that misuse could erode methodological standards by exacerbating existing biases and reducing reliability (Hanna et al. <span>2025</span>; Siemens et al. <span>2025</span>). These concerns are particularly relevant as current AI developments are often driven by commercial interests and, as such, are often opaque regarding limitations and lacking appropriate validation and evaluation. Overall, this undermines the reliability and replicability of AI-driven outputs.</p><p>To this end, Cochrane, the Campbell Collaboration, JBI, and the CEE have come together to form a joint AI Methods Group (AI Methods Group <span>2025</span>). The group officially supports the aims of RAISE (Thomas et al. <span>2025a</span>), which states that we need to work together to ensure AI does not compromise the principles of research integrity on which evidence synthesis was built. RAISE offers tailored recommendations for roles across the evidence synthesis ecosystem, from evidence synthesists to methodologists, from AI development teams to organizations or publishers involved in eviden
{"title":"Position Statement on Artificial Intelligence (AI) Use in Evidence Synthesis Across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025","authors":"Ella Flemyng, Anna Noel-Storr, Biljana Macura, Gerald Gartlehner, James Thomas, Joerg J. Meerpohl, Zoe Jordan, Jan Minx, Angelika Eisele-Metzger, Candyce Hamel, Paweł Jemioło, Kylie Porritt, Matthew Grainger","doi":"10.1002/cl2.70074","DOIUrl":"10.1002/cl2.70074","url":null,"abstract":"<p>Evidence syntheses, including systematic reviews, are a type of research that uses systematic, replicable methods to evaluate all available evidence on a specific question. They are built on the principles of research integrity, including rigor, transparency, and reproducibility. There is wide recognition that artificial intelligence (AI) and automation have the potential to transform the way we produce evidence syntheses, making the process significantly more efficient. However, this technology is potentially disruptive, characterized by opaque decision-making and black-box predictions, susceptible to overfitting, potentially embedded with algorithmic biases, and at risk of fabricated outputs and hallucinations. To safeguard evidence synthesis as the cornerstone of trusted, evidence-informed decision making, Cochrane, the Campbell Collaboration, JBI and the Collaboration for Environmental Evidence (CEE), have come together to collaborate on a responsible and pragmatic approach to AI use in evidence synthesis.</p><p>By AI, we mean different types of automation, as described within the Responsible use of AI in evidence SynthEsis recommendations (RAISE) (Thomas et al. <span>2025a</span>), specifically, “advanced technologies that enable machines to do highly complex tasks effectively – which would require intelligence if a person were to perform them.” This ranges from general automation applications, such as rule-based or trained machine learning algorithms, to more recent large language models and generative AI approaches.</p><p>Incorporating AI in evidence synthesis comes with challenges as well as opportunities. While it is clear we need to make better use of AI for evidence synthesis to become more timely, affordable, and sustainable, we must also acknowledge the environmental and social costs associated with some forms of AI, particularly large-scale language models. There are risks that misuse could erode methodological standards by exacerbating existing biases and reducing reliability (Hanna et al. <span>2025</span>; Siemens et al. <span>2025</span>). These concerns are particularly relevant as current AI developments are often driven by commercial interests and, as such, are often opaque regarding limitations and lacking appropriate validation and evaluation. Overall, this undermines the reliability and replicability of AI-driven outputs.</p><p>To this end, Cochrane, the Campbell Collaboration, JBI, and the CEE have come together to form a joint AI Methods Group (AI Methods Group <span>2025</span>). The group officially supports the aims of RAISE (Thomas et al. <span>2025a</span>), which states that we need to work together to ensure AI does not compromise the principles of research integrity on which evidence synthesis was built. RAISE offers tailored recommendations for roles across the evidence synthesis ecosystem, from evidence synthesists to methodologists, from AI development teams to organizations or publishers involved in eviden","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12603384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145507375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tarun M. Khanna, Diana Danilenko, Qianyi Wang, Luke A. Smith, Bhumika T. V., Aditya Narayan Rai, Jorge Sánchez Canales, Tim Repke, Max Callaghan, Mark Andor, Julian H. Elliott, Jan C. Minx
<p>Policymakers have little time left to prevent the worst impacts of climate change and limit global warming to well below two degrees. However, a systematic assessment of the available scientific evidence—that is up to date—is not always available to understand what climate policies work, to what extent, in what context, why, and for whom. This is also true for demand-side policies, including those that use behavioral change to reduce energy demand and the related carbon emissions. There is an ever-burgeoning literature on policy interventions that target behavioral change among households, with new insights and evidence of their efficacy in different contexts. This living systematic review (LSR) and network meta-analysis (NMA) synthesizes this evidence to provide timely, rigorous and up-to-date insights on this topic. Our LSR and NMA integrate the evidence available from multiple disciplines to answer the following questions: (1) to what extent can information, behavioral (including feedback, social comparison and motivation), and monetary based interventions reduce energy consumption of households; (2) what the relative effectiveness of interventions is; and (3) how effective are the combinations of different interventions. In doing so, we also pilot an LSR for climate policy solutions and share learnings with the community. To fulfill these objectives, we searched the academic and gray literature for experimental and quasi-experimental studies that quantitatively assessed the impact of either behavioral, monetary, or information interventions (or a combination of these) on energy consumption (including electricity and heat) of the households in residential buildings. We searched the relevant databases: Web of Science Core Collections Citation Indexes, Scopus, JSTOR, RePec, Google Scholar, and gray literature repository Policy Commons to retrieve over 109,000 potentially relevant article abstracts and apply machine learning algorithms to identify the most likely relevant papers. Note that with this update, that includes the relevant literature published till end of December 2024, we added roughly 53,000 potentially relevant documents to the previously existing pool of potentially relevant literature from Khanna et al. (2021). A team of four reviewers screened the titles and abstracts of studies identified as being potentially relevant by the machine learning algorithm, with full-text assessments and double-coded data collection following for a set of included studies. The effect sizes reported by different studies were harmonized to Cohen's d for synthesis. We used a multilevel random effects model and NMA for calculating the average intervention effect. We adjust our estimates for possible small-study effects (publication bias). The NMA allows us to visualize the relative efficacy of the interventions through rankograms and cumulative ranking probability plots. Unlike previous meta-analyses in this field of research, this study also implemen
{"title":"Behavioral, Information, and Monetary Interventions to Reduce Energy Consumption in Households: A Living Systematic Review and Network Meta-Analysis","authors":"Tarun M. Khanna, Diana Danilenko, Qianyi Wang, Luke A. Smith, Bhumika T. V., Aditya Narayan Rai, Jorge Sánchez Canales, Tim Repke, Max Callaghan, Mark Andor, Julian H. Elliott, Jan C. Minx","doi":"10.1002/cl2.70070","DOIUrl":"https://doi.org/10.1002/cl2.70070","url":null,"abstract":"<p>Policymakers have little time left to prevent the worst impacts of climate change and limit global warming to well below two degrees. However, a systematic assessment of the available scientific evidence—that is up to date—is not always available to understand what climate policies work, to what extent, in what context, why, and for whom. This is also true for demand-side policies, including those that use behavioral change to reduce energy demand and the related carbon emissions. There is an ever-burgeoning literature on policy interventions that target behavioral change among households, with new insights and evidence of their efficacy in different contexts. This living systematic review (LSR) and network meta-analysis (NMA) synthesizes this evidence to provide timely, rigorous and up-to-date insights on this topic. Our LSR and NMA integrate the evidence available from multiple disciplines to answer the following questions: (1) to what extent can information, behavioral (including feedback, social comparison and motivation), and monetary based interventions reduce energy consumption of households; (2) what the relative effectiveness of interventions is; and (3) how effective are the combinations of different interventions. In doing so, we also pilot an LSR for climate policy solutions and share learnings with the community. To fulfill these objectives, we searched the academic and gray literature for experimental and quasi-experimental studies that quantitatively assessed the impact of either behavioral, monetary, or information interventions (or a combination of these) on energy consumption (including electricity and heat) of the households in residential buildings. We searched the relevant databases: Web of Science Core Collections Citation Indexes, Scopus, JSTOR, RePec, Google Scholar, and gray literature repository Policy Commons to retrieve over 109,000 potentially relevant article abstracts and apply machine learning algorithms to identify the most likely relevant papers. Note that with this update, that includes the relevant literature published till end of December 2024, we added roughly 53,000 potentially relevant documents to the previously existing pool of potentially relevant literature from Khanna et al. (2021). A team of four reviewers screened the titles and abstracts of studies identified as being potentially relevant by the machine learning algorithm, with full-text assessments and double-coded data collection following for a set of included studies. The effect sizes reported by different studies were harmonized to Cohen's d for synthesis. We used a multilevel random effects model and NMA for calculating the average intervention effect. We adjust our estimates for possible small-study effects (publication bias). The NMA allows us to visualize the relative efficacy of the interventions through rankograms and cumulative ranking probability plots. Unlike previous meta-analyses in this field of research, this study also implemen","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cl2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xingbo Wang, Samantha L. Huey, Rui Sheng, Saurabh Mehta, Fei Wang
The explosion of scientific literature has made the efficient and accurate extraction of structured data a critical component for advancing scientific knowledge and supporting evidence-based decision-making. However, existing tools often struggle to extract and structure multimodal, varied, and inconsistent information across documents into standardized formats. We introduce SciDaSynth, a novel interactive system powered by large language models that automatically generates structured data tables according to users' queries by integrating information from diverse sources, including text, tables, and figures. Furthermore, SciDaSynth supports efficient table data validation and refinement, featuring multi-faceted visual summaries and semantic grouping capabilities to resolve cross-document data inconsistencies. A within-subjects study with nutrition and NLP researchers demonstrates SciDaSynth's effectiveness in producing high-quality structured data more efficiently than baseline methods. We discuss design implications for human–AI collaborative systems supporting data extraction tasks.
{"title":"SciDaSynth: Interactive Structured Data Extraction From Scientific Literature With Large Language Model","authors":"Xingbo Wang, Samantha L. Huey, Rui Sheng, Saurabh Mehta, Fei Wang","doi":"10.1002/cl2.70073","DOIUrl":"10.1002/cl2.70073","url":null,"abstract":"<p>The explosion of scientific literature has made the efficient and accurate extraction of structured data a critical component for advancing scientific knowledge and supporting evidence-based decision-making. However, existing tools often struggle to extract and structure multimodal, varied, and inconsistent information across documents into standardized formats. We introduce SciDaSynth, a novel interactive system powered by large language models that automatically generates structured data tables according to users' queries by integrating information from diverse sources, including text, tables, and figures. Furthermore, SciDaSynth supports efficient table data validation and refinement, featuring multi-faceted visual summaries and semantic grouping capabilities to resolve cross-document data inconsistencies. A within-subjects study with nutrition and NLP researchers demonstrates SciDaSynth's effectiveness in producing high-quality structured data more efficiently than baseline methods. We discuss design implications for human–AI collaborative systems supporting data extraction tasks.</p>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12581027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145446136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Addressing the climate change and biodiversity loss crises while ensuring livelihoods are not negatively affected is a matter that requires urgent action. A recently published Evidence Gap Map (EGM) identified no recent systematic reviews on land management interventions. Drawing from this EGM, the review aims to examine and synthesise the latest evidence on what works, how, and at what cost to improve environmental and human welfare outcomes in land management in low- and middle-income countries. We will address the following research questions: (1) What are the effects of protected areas, land rights and decentralisation interventions on environmental and poverty outcomes? Do effects vary by population, location, or other factors? (2) What are the barriers and enablers that impact the effectiveness of these interventions? (3) What is the cost-effectiveness of these interventions? The set of interventions will be based on the studies identified in the EGM, and we will search, appraise and synthesise additional evidence on influencing factors and cost data.
{"title":"PROTOCOL: The Effects of Land Management Policies on the Environment and People in Low- and Middle-Income Countries: A Systematic Review","authors":"Pierre Marion, Ingunn Storhaug, Sanghwa Lee, Claudia Romero, Constanza Gonzalez Parrao, Birte Snilstveit","doi":"10.1002/cl2.70062","DOIUrl":"10.1002/cl2.70062","url":null,"abstract":"<p>Addressing the climate change and biodiversity loss crises while ensuring livelihoods are not negatively affected is a matter that requires urgent action. A recently published Evidence Gap Map (EGM) identified no recent systematic reviews on land management interventions. Drawing from this EGM, the review aims to examine and synthesise the latest evidence on what works, how, and at what cost to improve environmental and human welfare outcomes in land management in low- and middle-income countries. We will address the following research questions: (1) What are the effects of protected areas, land rights and decentralisation interventions on environmental and poverty outcomes? Do effects vary by population, location, or other factors? (2) What are the barriers and enablers that impact the effectiveness of these interventions? (3) What is the cost-effectiveness of these interventions? The set of interventions will be based on the studies identified in the EGM, and we will search, appraise and synthesise additional evidence on influencing factors and cost data.</p>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12558594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sara Valdebenito, Hannah Gaffney, Maria Jose Arosemena-Burbano, Sydney Hitchcock, Darrick Jolliffe, Alex Sutherland
<p>School exclusion—commonly referred to as suspension—is a disciplinary response employed by school authorities to address student misbehaviour. Typically, it involves temporary removal from regular teaching or, in more serious cases, complete removal from the school premises. A substantial body of research has associated exclusion with adverse developmental outcomes. In response, various school-based interventions have been developed to reduce exclusion rates. While some programmes have shown promising effects, the evidence on their effectiveness remains inconclusive. This mixed-methods systematic review and multi-level meta-analysis updates the previous review by Valdebenito et al. (2018), which included literature published between 1980 and 2015. The present update extends the evidence base by including studies until 2022. The primary aim of this review was to assess the effectiveness of school-based interventions in reducing disciplinary exclusions, with secondary aims focused on related behavioural outcomes including conduct problems, delinquency, and substance use. Systematic searches conducted between November and December 2022 yielded over 11,000 references for quantitative studies. Following title and abstract screening, 777 records were reviewed at full text by two independent coders. Thirty-two studies met the inclusion criteria for meta-analysis, comprising 2765 effect sizes from 67 primary evaluations (1980–2022) and representing approximately 394,242 students. Meta-analysis was conducted using a multilevel random-effects model with robust variance estimation to account for the nested structure of the data. Quantitative impact evaluations were eligible if they used a randomised controlled or quasi-experimental design, included both a control group and pre/post-test data, and used statistical methods to minimise selection bias (e.g., propensity score matching or matched cohort design). Studies were excluded if they exhibited substantial baseline differences between treatment and control groups. The qualitative synthesis explored implementation barriers and facilitators based on nine UK-based process evaluations, identified through searches completed in September 2023. Process evaluations were included if they focused on the perceptions of stakeholders—teachers, students, or school leadership—within UK schools. Data collection followed two stages: initial selection based on titles, abstracts, and keywords, followed by full-text review. Two independent coders applied inclusion criteria, extracted data, and resolved discrepancies with the principal investigators. All steps were documented to inform the PRISMA flow chart. To evaluate interventions reducing school exclusions, we conducted a multilevel meta-analysis using robust variance estimation. We explored heterogeneity via meta-regression (e.g., gender, intervention type), conducted sensitivity analyses for outliers and correlation structures, and assessed quality data using the EPOC
{"title":"School-Based Interventions for Reducing Disciplinary School Exclusion. An Updated Systematic Review","authors":"Sara Valdebenito, Hannah Gaffney, Maria Jose Arosemena-Burbano, Sydney Hitchcock, Darrick Jolliffe, Alex Sutherland","doi":"10.1002/cl2.70063","DOIUrl":"10.1002/cl2.70063","url":null,"abstract":"<p>School exclusion—commonly referred to as suspension—is a disciplinary response employed by school authorities to address student misbehaviour. Typically, it involves temporary removal from regular teaching or, in more serious cases, complete removal from the school premises. A substantial body of research has associated exclusion with adverse developmental outcomes. In response, various school-based interventions have been developed to reduce exclusion rates. While some programmes have shown promising effects, the evidence on their effectiveness remains inconclusive. This mixed-methods systematic review and multi-level meta-analysis updates the previous review by Valdebenito et al. (2018), which included literature published between 1980 and 2015. The present update extends the evidence base by including studies until 2022. The primary aim of this review was to assess the effectiveness of school-based interventions in reducing disciplinary exclusions, with secondary aims focused on related behavioural outcomes including conduct problems, delinquency, and substance use. Systematic searches conducted between November and December 2022 yielded over 11,000 references for quantitative studies. Following title and abstract screening, 777 records were reviewed at full text by two independent coders. Thirty-two studies met the inclusion criteria for meta-analysis, comprising 2765 effect sizes from 67 primary evaluations (1980–2022) and representing approximately 394,242 students. Meta-analysis was conducted using a multilevel random-effects model with robust variance estimation to account for the nested structure of the data. Quantitative impact evaluations were eligible if they used a randomised controlled or quasi-experimental design, included both a control group and pre/post-test data, and used statistical methods to minimise selection bias (e.g., propensity score matching or matched cohort design). Studies were excluded if they exhibited substantial baseline differences between treatment and control groups. The qualitative synthesis explored implementation barriers and facilitators based on nine UK-based process evaluations, identified through searches completed in September 2023. Process evaluations were included if they focused on the perceptions of stakeholders—teachers, students, or school leadership—within UK schools. Data collection followed two stages: initial selection based on titles, abstracts, and keywords, followed by full-text review. Two independent coders applied inclusion criteria, extracted data, and resolved discrepancies with the principal investigators. All steps were documented to inform the PRISMA flow chart. To evaluate interventions reducing school exclusions, we conducted a multilevel meta-analysis using robust variance estimation. We explored heterogeneity via meta-regression (e.g., gender, intervention type), conducted sensitivity analyses for outliers and correlation structures, and assessed quality data using the EPOC","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12541690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Relocation processes are increasingly considered local adaptations to flooding. While relocation can offer many benefits, it also leads to socio-economic and socio-psychological consequences. Moreover, it tends to place greater pressure on populations experiencing socio-territorial inequities compared to other households. It is therefore important to assess the direct and indirect impacts of relocation to consider its application from a more just and equitable perspective. The objective of this scoping review is to document the impacts of flood-induced residential relocation processes on populations facing socio-territorial inequities in North America and Europe. It also seeks to categorize the challenges faced by planners in relocation processes. This will help us better assess the relevance of relocation as an adaptation measure to climate change for populations facing socio-territorial inequities. This scoping review will be conducted in accordance with the methodological guide JBI Manual for Evidence Synthesis (Aromataris and Munn 2020). A systematic search will first be developed in collaboration with an expert librarian. Five databases will be searched: Erudit, Cairn, Web of Science, GreenFILE (EBSCO), and GeoBase (Engineering Village). Gray literature will be collected from Policy Commons, Open Access Theses and Dissertations, Google Scholar, Google, and relevant governmental websites. A screening of the studies and documents obtained will then be carried out by two independent reviewers to retain the relevant documents for the scoping review. If needed, the involvement of a third reviewer will be solicited. The data relevant to our question will then be extracted, using a tool created by the authors, and then analyzed and presented in narrative and tabular formats. The results of this scoping review will be used to discuss and share insights and lessons learned. It will allow us to draw conclusions on the impacts of residential relocation processes on populations facing socio-territorial inequities and to identify best practices for land-use planning in the context of climate change.
重新安置过程越来越多地被认为是当地对洪水的适应。虽然搬迁可以带来许多好处，但它也会导致社会经济和社会心理后果。此外，与其他家庭相比，它往往对经历社会-领土不平等的人口施加更大的压力。因此，重要的是评估搬迁的直接和间接影响，以便从更公正和公平的角度考虑其适用。本次范围审查的目的是记录洪水引起的居住重新安置过程对北美和欧洲面临社会领土不平等的人口的影响。它还设法对规划人员在搬迁过程中所面临的挑战进行分类。这将有助于我们更好地评估搬迁作为一种适应气候变化措施对面临社会-领土不平等的人口的相关性。本次范围审查将按照JBI证据合成手册（Aromataris and Munn 2020）的方法学指南进行。一个系统的搜索将首先与一位专家图书馆员合作开发。五个数据库将被检索：Erudit， Cairn, Web of Science, GreenFILE （EBSCO）和GeoBase （Engineering Village）。灰色文献将从政策共享、开放存取论文、谷歌学者、谷歌和相关政府网站收集。然后将由两名独立审稿人对获得的研究和文件进行筛选，以保留相关文件用于范围审查。如有需要，将征求第三方审稿人的参与。然后将使用作者创建的工具提取与我们问题相关的数据，然后分析并以叙述和表格格式呈现。此范围审查的结果将用于讨论和分享见解和经验教训。它将使我们能够得出关于住宅搬迁过程对面临社会领土不平等的人口的影响的结论，并确定气候变化背景下土地使用规划的最佳做法。
{"title":"PROTOCOL: The Impact of Relocation Processes on Populations Facing Socio-Territorial Inequities: A Scoping Review Protocol: A Systematic Review","authors":"Pascale Chagnon, Audate Pierre-Paul, Geneviève Cloutier, Marianne Demers-Desmarais","doi":"10.1002/cl2.70072","DOIUrl":"10.1002/cl2.70072","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 <p>Relocation processes are increasingly considered local adaptations to flooding. While relocation can offer many benefits, it also leads to socio-economic and socio-psychological consequences. Moreover, it tends to place greater pressure on populations experiencing socio-territorial inequities compared to other households. It is therefore important to assess the direct and indirect impacts of relocation to consider its application from a more just and equitable perspective. The objective of this scoping review is to document the impacts of flood-induced residential relocation processes on populations facing socio-territorial inequities in North America and Europe. It also seeks to categorize the challenges faced by planners in relocation processes. This will help us better assess the relevance of relocation as an adaptation measure to climate change for populations facing socio-territorial inequities. This scoping review will be conducted in accordance with the methodological guide <i>JBI Manual for Evidence Synthesis</i> (Aromataris and Munn 2020). A systematic search will first be developed in collaboration with an expert librarian. Five databases will be searched: Erudit, Cairn, Web of Science, GreenFILE (EBSCO), and GeoBase (Engineering Village). Gray literature will be collected from Policy Commons, Open Access Theses and Dissertations, Google Scholar, Google, and relevant governmental websites. A screening of the studies and documents obtained will then be carried out by two independent reviewers to retain the relevant documents for the scoping review. If needed, the involvement of a third reviewer will be solicited. The data relevant to our question will then be extracted, using a tool created by the authors, and then analyzed and presented in narrative and tabular formats. The results of this scoping review will be used to discuss and share insights and lessons learned. It will allow us to draw conclusions on the impacts of residential relocation processes on populations facing socio-territorial inequities and to identify best practices for land-use planning in the context of climate change.</p>\u0000 </section>\u0000 </div>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145293983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jenny B. O'Connor, Shane S. George, Morgan R. Klein, Nicklaus R. Herbst, Lauren Hoffman, Melis Lydston, John F. Kelly, Emily A. Hennessy
This is the protocol for a Campbell systematic review. The objectives are as follows: (1) What qualitative themes and sub-themes characterize the experience of parenting and addiction recovery in parents who self-identify as being in recovery or who are identified by the study authors as a sample in recovery (e.g., in the process/have resolved a SUD as indicated by stopping use of substances)? To what extent do these themes reveal how parents incorporate parenting and recovery identities as a core characteristic of oneself? (2) In what ways does gender shape the thematic evidence on how parenting identities intersect with recovery identities? (3) What are the barriers and facilitators for parents in accessing and utilizing addiction recovery services?
{"title":"Protocol: Review Title: Investigating the Intersection of Parenting and Substance Use Recovery Identities: A Qualitative Scoping Review: A Systematic Review","authors":"Jenny B. O'Connor, Shane S. George, Morgan R. Klein, Nicklaus R. Herbst, Lauren Hoffman, Melis Lydston, John F. Kelly, Emily A. Hennessy","doi":"10.1002/cl2.70068","DOIUrl":"https://doi.org/10.1002/cl2.70068","url":null,"abstract":"<p>This is the protocol for a Campbell systematic review. The objectives are as follows: (1) What qualitative themes and sub-themes characterize the experience of parenting and addiction recovery in parents who self-identify as being in recovery or who are identified by the study authors as a sample in recovery (e.g., in the process/have resolved a SUD as indicated by stopping use of substances)? To what extent do these themes reveal how parents incorporate parenting and recovery identities as a core characteristic of oneself? (2) In what ways does gender shape the thematic evidence on how parenting identities intersect with recovery identities? (3) What are the barriers and facilitators for parents in accessing and utilizing addiction recovery services?</p>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cl2.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emerging evidence suggests that coronavirus disease 2019 (COVID-19) infection may cause diabetes mellitus in patients without a prior history of the condition.
Objective
This review aims to determine the incidence of new-onset diabetes mellitus in COVID-19 patients compared to individuals without COVID-19, including rates of diabetic ketoacidosis, hyperglycaemia, mortality, and intensive care unit admission. Subgroup analyses will investigate patients receiving corticosteroid therapy for COVID-19, patients admitted to hospital due to COVID-19, and the incidence of new-onset diabetes mellitus in relation to diabetes mellitus worldwide. The incidence of new-onset diabetes mellitus post-infection after a 6–12-month follow-up will also be reported.
Methods
This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and is registered in PROSPERO (CRD42023457569). Eligible studies will include published and peer-reviewed cohort studies in English, after 1 December 2019. PubMed, Medline, Embase, and Scopus will be systematically searched. Included studies should compare new-onset diabetes mellitus incidence in COVID-19 patients against a control group without COVID-19. Two independent reviewers will extract data from included studies and assess risk of bias using the Newcastle-Ottawa quality assessment scale. A random-effects meta-analysis will compare the relative risk of new-onset diabetes mellitus post COVID-19 infection compared to non-infected individuals.
Conclusion
The findings of this review will contribute to understanding the bidirectional relationship between COVID-19 and diabetes mellitus and inform clinical management strategies for patients at risk.
{"title":"PROTOCOL: New-Onset Diabetes Mellitus Post COVID-19 Infection: A Protocol for Systematic Review and Meta-Analysis: A Systematic Review","authors":"Emma Cocking, Joseph Daher, Majid Alabbood","doi":"10.1002/cl2.70069","DOIUrl":"10.1002/cl2.70069","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Emerging evidence suggests that coronavirus disease 2019 (COVID-19) infection may cause diabetes mellitus in patients without a prior history of the condition.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This review aims to determine the incidence of new-onset diabetes mellitus in COVID-19 patients compared to individuals without COVID-19, including rates of diabetic ketoacidosis, hyperglycaemia, mortality, and intensive care unit admission. Subgroup analyses will investigate patients receiving corticosteroid therapy for COVID-19, patients admitted to hospital due to COVID-19, and the incidence of new-onset diabetes mellitus in relation to diabetes mellitus worldwide. The incidence of new-onset diabetes mellitus post-infection after a 6–12-month follow-up will also be reported.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and is registered in PROSPERO (CRD42023457569). Eligible studies will include published and peer-reviewed cohort studies in English, after 1 December 2019. PubMed, Medline, Embase, and Scopus will be systematically searched. Included studies should compare new-onset diabetes mellitus incidence in COVID-19 patients against a control group without COVID-19. Two independent reviewers will extract data from included studies and assess risk of bias using the Newcastle-Ottawa quality assessment scale. A random-effects meta-analysis will compare the relative risk of new-onset diabetes mellitus post COVID-19 infection compared to non-infected individuals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The findings of this review will contribute to understanding the bidirectional relationship between COVID-19 and diabetes mellitus and inform clinical management strategies for patients at risk.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Systematic Review Registration</h3>\u0000 \u0000 <p>PROSPERO CRD42023457569.</p>\u0000 </section>\u0000 </div>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fleur O'Hare, Sujani Thrimawithana, Aimee Clague, Eden G. Robertson, David Foran, Caroline Ondracek, Camille Paynter, Tessa Saunders, Lauren N. Ayton
An equitable and accessible informed consent process is needed to support agency and mutual decision-making. This systematic review aims to gather and characterise the evidence supporting methods that enhance inclusion and accessibility in informed consent practice for people with vision and/or hearing support needs. It will address the research question: what strategies are being used to enhance inclusive consent practice for people with vision impairment, people who are d/Deaf or hard of hearing, and people who are d/Deafblind. Additionally, the review aims to generate recommendations to facilitate the uptake of accessibility practices within informed consent procedures. This systematic review will build on current evidence for the positive impact of intentional actions to support accessible communication and information exchange. It may guide future work on intervention development and primary research in improving equity in clinical care and research.
{"title":"Protocol: Strategies to Enhance Inclusion in Informed Consent Practice for People With Vision and/or Hearing Support Needs: A Systematic Review","authors":"Fleur O'Hare, Sujani Thrimawithana, Aimee Clague, Eden G. Robertson, David Foran, Caroline Ondracek, Camille Paynter, Tessa Saunders, Lauren N. Ayton","doi":"10.1002/cl2.70065","DOIUrl":"https://doi.org/10.1002/cl2.70065","url":null,"abstract":"<p>An equitable and accessible informed consent process is needed to support agency and mutual decision-making. This systematic review aims to gather and characterise the evidence supporting methods that enhance inclusion and accessibility in informed consent practice for people with vision and/or hearing support needs. It will address the research question: what strategies are being used to enhance inclusive consent practice for people with vision impairment, people who are d/Deaf or hard of hearing, and people who are d/Deafblind. Additionally, the review aims to generate recommendations to facilitate the uptake of accessibility practices within informed consent procedures. This systematic review will build on current evidence for the positive impact of intentional actions to support accessible communication and information exchange. It may guide future work on intervention development and primary research in improving equity in clinical care and research.</p>","PeriodicalId":36698,"journal":{"name":"Campbell Systematic Reviews","volume":"21 4","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cl2.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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