The Impact of Refugee Shocks on Host Countries: A Scoping Review | Comparative Population Studies

Quality of life measures in treatment for active substance misuse: a scoping review - Quality of Life Research

Purpose Unlike previous reviews on quality of life (QoL) and substance use disorders (SUD), this scoping review focuses on QoL within the context of SUD treatment and active substance misuse. This review delineates and describes QoL measures, assesses their application, and explores the influence of demographics on QoL outcomes. Methods Five databases were searched (EMBASE, Web of Science, PsycINFO, PubMed, and Scopus) using keywords related to QoL, clinician-verified SUD, and SUD treatment. All studies were published in English before June 1st, 2025. Two reviewers screened abstracts and full text for eligibility and study elements. The first author extracted data regarding methodology, substance use, treatment, and QoL measures. The PRISMA-ScR checklist guided this study and the reporting process for rigor and transparency. Results Of the 11,078 initially identified articles, 108 were extracted for analysis. QoL measures accounted for multiple domains (e.g., physical, psychological, social, environmental, independence, security, and spiritual). Studies included male and female participants, all over the age of 18. Results found that many studies under-reported race, ethnicity, and cultural contexts, as well as high variability in the definition of QoL domains and standardization across treatment locations and geographic regions. Studies from Asia primarily showed preference for the WHOQOL-BREF measure compared to North America and Europe, which showed varied preference for measures. The most common QoL domains assessed were psychological, physical, and social. Conclusion This review highlights the need to operationalize QoL domains and assessment approaches in SUD treatment research to facilitate meaningful comparisons and enhance our understanding of treatment effectiveness.

Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach - BMC Medical Research Methodology

Background Scoping reviews are a relatively new approach to evidence synthesis and currently there exists little guidance regarding the decision to choose between a systematic review or scoping review approach when synthesising evidence. The purpose of this article is to clearly describe the differences in indications between scoping reviews and systematic reviews and to provide guidance for when a scoping review is (and is not) appropriate. Results Researchers may conduct scoping reviews instead of systematic reviews where the purpose of the review is to identify knowledge gaps, scope a body of literature, clarify concepts or to investigate research conduct. While useful in their own right, scoping reviews may also be helpful precursors to systematic reviews and can be used to confirm the relevance of inclusion criteria and potential questions. Conclusions Scoping reviews are a useful tool in the ever increasing arsenal of evidence synthesis approaches. Although conducted for different purposes compared to systematic reviews, scoping reviews still require rigorous and transparent methods in their conduct to ensure that the results are trustworthy. Our hope is that with clear guidance available regarding whether to conduct a scoping review or a systematic review, there will be less scoping reviews being performed for inappropriate indications better served by a systematic review, and vice-versa.

Women’s experiences of living with adenomyosis and perceptions of the diagnostic journey: a scoping review

Objectives Uterine adenomyosis is a common gynaecological disease that can be debilitating. It is poorly understood and may be overlooked in clinical settings. A research gap exists as there are currently no published scoping reviews on perceptions and experiences early in the illness course. As part of a professional doctorate thesis, the aim of this review is to systematically retrieve and describe available literature, exploring the impact of living with adenomyosis and perceptions of the diagnostic journey. Design A scoping review is conducted using JBI methodology. Data sources Medline, CINAHL Plus, Web of Science, Google Scholar, Cochrane library, JBI and PROSPERO databases, EThOS online and Google. Searches were made from database inception to July 2023. Eligibility criteria The characteristics of the evidence sourced were deliberately broad. Studies exploring the experiences and perceptions of women diagnosed with adenomyosis were considered. Data extraction and synthesis Titles and abstracts were initially screened. Subsequently, eligibility was clarified through methods section inspection, and the remaining studies were read in depth. A manual hand-search of references of selected studies was conducted. Prespecified data were extracted, charted and categorised into themes. Results Six eligible studies were found, with themes describing impact and burdens, as well as several categories of unsupported needs. No studies specifically focused on perceptions of the diagnostic journey, but some eligible studies made minor reference to this and are included. Conclusions This review highlights the profound impact of adenomyosis and is the first to explore the lived experiences and the diagnostic journey. Understanding the burdens of disease in terms of perceptions and lived experience in combination with the experiences of diagnostic interactions is vital to improving diagnostic pathways. Education with improved multidisciplinary collaboration and further qualitative and case study research will be crucial to achieve this goal. Registration A priori protocol was registered (<https://doi.org/10.17605/OSF.IO/2UDYN>) and published (<https://doi.org/10.1136/bmjopen-2023-075316>). All data relevant to the study are included in the article or uploaded as supplementary information.

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions - BMC Medical Research Methodology

Background Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (“missing completely at random”, “missing at random” [MAR], “missing not at random”) are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation. Methods We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically. Results Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis. Conclusion Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.