Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple-stressor experiments.

Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.

Exploring undiscovered public knowledge in neuroscience.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

Public participation in healthcare students' education: An umbrella review.

BACKGROUND: An often-hidden element in healthcare students' education is the pedagogy of public involvement, yet public participation can result in deep learning for students with positive impacts on the public who participate. OBJECTIVE: This article aimed to synthesize published literature reviews that described the impact of public participation in healthcare students' education. SEARCH STRATEGY: We searched MEDLINE, EMBASE, ERIC, PsychINFO, CINAHL, PubMed, JBI Database of Systematic Reviews and Implementation Reports, the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects and the PROSPERO register for literature reviews on public participation in healthcare students' education. INCLUSION CRITERIA: Reviews published in the last 10 years were included if they described patient or public participation in healthcare students' education and reported the impacts on students, the public, curricula or healthcare systems. DATA EXTRACTION AND SYNTHESIS: Data were extracted using a predesigned data extraction form and narratively synthesized. MAIN RESULTS: Twenty reviews met our inclusion criteria reporting on outcomes related to students, the public, curriculum and future professional practice. DISCUSSION AND CONCLUSION: Our findings raise awareness of the benefits and challenges of public participation in healthcare students' education and may inform future research exploring how public participation can best be utilized in higher education. PATIENT OR PUBLIC CONTRIBUTION: This review was inspired by conversations with public healthcare consumers who saw value in public participation in healthcare students' education. Studies included involved public participants, providing a deeper understanding of the impacts of public participation in healthcare students' education.

Factors influencing nurse manager retention, intent to stay or leave and turnover: A systematic review update.

AIMS: To understand factors influencing nurse manager retention or intention to leave, develop a preliminary theoretical model and identify strategies and interventions for workforce planning. DESIGN: Systematic review update of literature with integrated design of mixed research synthesis. METHODS: We included peer-reviewed articles examining factors influencing retention or intention to leave for front-line, middle or patient care nurse managers. Authors independently screened articles for inclusion and assessed included articles for quality. We adhered to a convergent synthesis approach. DATA SOURCES: Nine databases included MEDLINE, EMBASE, PsychINFO, CINAHL Plus with Full Text, ERIC, Health Source Nursing/Academic Edition, Scopus, ProQuest Dissertations and Theses and LILACS in January 2023. RESULTS: Thirty-five studies published between 1990 and 2022, 22 quantitative and 13 qualitative or mixed methods, were included. 155 factors influencing nurse manager retention, intention to stay, or turnover were reported. Job satisfaction was most frequently examined (n = 7), followed by factors such as empowerment (n = 3), decision-making (n = 3) and resilience (n = 2). We developed a preliminary theoretical model demonstrating staff relations and leadership, organizational and job characteristics, socio-demographics, personal characteristics, well-being and nurse manager relationship with work influence managers' intention to stay or to leave. CONCLUSIONS: Nurse managers who were empowered, satisfied with their work, received constructive feedback and found meaning in their roles showed greater intent to stay. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurse administrators can ensure managers are provided with authority to make decisions that promote autonomy. Work cultures that allow for regular meaningful constructive feedback from staff and leaders may contribute to nurse managers feeling valued. IMPACT: Understanding factors that influence job retention or intention to leave may help nurse managers and their supervisors identify areas for strategy and intervention design to ensure sustainability of this workforce. REPORTING METHOD: PRISMA 2020 Guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Meta-analysis in a digitalized world: A step-by-step primer.

In recent years, much research and many data sources have become digital. Some advantages of digital or Internet-based research, compared to traditional lab research (e.g., comprehensive data collection and storage, availability of data) are ideal for an improved meta-analyses approach.In the meantime, in meta-analyses research, different types of meta-analyses have been developed to provide research syntheses with accurate quantitative estimations. Due to its rich and unique palette of corrections, we recommend to using the Schmidt and Hunter approach for meta-analyses in a digitalized world. Our primer shows in a step-by-step fashion how to conduct a high quality meta-analysis considering digital data and highlights the most obvious pitfalls (e.g., using only a bare-bones meta-analysis, no data comparison) not only in aggregation of the data, but also in the literature search and coding procedure which are essential steps in any meta-analysis. Thus, this primer of meta-analyses is especially suited for a situation where much of future research is headed to: digital research. To map Internet-based research and to reveal any research gap, we further synthesize meta-analyses on Internet-based research (15 articles containing 24 different meta-analyses, on 745 studies, with 1,601 effect sizes), resulting in the first mega meta-analysis of the field. We found a lack of individual participant data (e.g., age and nationality). Hence, we provide a primer for high-quality meta-analyses and mega meta-analyses that applies to much of coming research and also basic hands-on knowledge to conduct or judge the quality of a meta-analyses in a digitalized world.

Bayesian evidence synthesis as a flexible alternative to meta-analysis: A simulation study and empirical demonstration.

Synthesizing results across multiple studies is a popular way to increase the robustness of scientific findings. The most well-known method for doing this is meta-analysis. However, because meta-analysis requires conceptually comparable effect sizes with the same statistical form, meta-analysis may not be possible when studies are highly diverse in terms of their research design, participant characteristics, or operationalization of key variables. In these situations, Bayesian evidence synthesis may constitute a flexible and feasible alternative, as this method combines studies at the hypothesis level rather than at the level of the effect size. This method therefore poses less constraints on the studies to be combined. In this study, we introduce Bayesian evidence synthesis and show through simulations when this method diverges from what would be expected in a meta-analysis to help researchers correctly interpret the synthesis results. As an empirical demonstration, we also apply Bayesian evidence synthesis to a published meta-analysis on statistical learning in people with and without developmental language disorder. We highlight the strengths and weaknesses of the proposed method and offer suggestions for future research.

A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations.

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of 'missingness'.

Task-based functional magnetic resonance imaging prediction of postsurgical cognitive outcomes in temporal lobe epilepsy: A systematic review, meta-analysis, and new data.

Task-based functional magnetic resonance imaging (tfMRI) has developed as a common alternative in epilepsy surgery to the intracarotid amobarbital procedure, also known as the Wada procedure. Prior studies have implicated tfMRI as a comparable predictor of postsurgical cognitive outcomes. However, the predictive validity of tfMRI has not been established. This preregistered systematic review and meta-analysis (CRD42020183563) synthesizes the literature predicting postsurgical cognitive outcomes in temporal lobe epilepsy (TLE) using tfMRI. The PubMed and PsycINFO literature databases were queried for English-language articles published between January 1, 2009 and December 31, 2020 associating tfMRI laterality indices or symmetry of task activation with outcomes in TLE. Their references were reviewed for additional relevant literature, and unpublished data from our center were incorporated. Nineteen studies were included in the meta-analysis. tfMRI studies predicted postsurgical cognitive outcomes in left TLE ( ρ ̂ = -.27, 95% confidence interval [CI] = -.32 to -.23) but not right TLE ( ρ ̂ = -.02, 95% CI = -.08 to .03). Among studies of left TLE, language tfMRI studies were more robustly predictive of postsurgical cognitive outcomes ( ρ ̂ = -.27, 95% CI = -.33 to -.20) than memory tfMRI studies ( ρ ̂ = -.27, 95% CI = -.43 to -.11). Further moderation by cognitive outcome domain indicated language tfMRI predicted confrontation naming ( ρ ̂ = -.32, 95% CI = -.41 to -.22) and verbal memory ( ρ ̂ = -.26, 95% CI = -.35 to -.17) outcomes, whereas memory tfMRI forecasted only verbal memory outcomes ( ρ ̂ = -.37, 95% CI = -.57 to -.18). Surgery type, birth sex, level of education, age at onset, disease duration, and hemispheric language dominance moderated study outcomes. Sensitivity analyses suggested the interval of postsurgical follow-up, and reporting and methodological practices influenced study outcomes as well. These findings intimate tfMRI is a modest predictor of outcomes in left TLE that should be considered in the context of a larger surgical workup.

Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population.

We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.

Global mental health experiences of single mothers: A mixed methods research synthesis.

AIM: The aim of this research is to synthesize findings from primary studies (quantitative and qualitative) that investigated the global mental health experiences of single mothers to provide a deeper understanding to better care and respond to the support needs of single mothers. DESIGN: Hayvaert et al.'s mixed methods research synthesis approach. DATA SOURCES: The search process in the following databases, CINAHL, PsycINFO, and Scopus resulted in eight high-quality studies (5 qualitative and 3 quantitative) published between June 2016 and July 2021. REVIEW METHODS: Descriptive statistics and instrument scores were provided in summary form. Themes were analysed using Krippendorff's content analysis. A joint display was provided to reveal a complementary relationship between two different data sets. RESULTS: A total of 348 single mothers participated. Amongst the pooled sample, women identified as: Japanese (n = 174), Israeli (n = 147), Black African (n = 18), African American (n = 9), Native American (n = 5), Burundian-Australian (n = 8), UK British (n = 12), Asian (n = 3), South Korean (n = 7), Indian (n = 2), Malaysian (n = 44), Hispanic/Latina (n = 1) and Eastern European (n = 3). Four themes were identified: (1) Learning to let go of the past, (2) It takes a whole village: Importance of social support, (3) Seeking a self-reliant life: Challenges with balancing career & childcare and (4) Finding strength within: Personal growth. Only one intervention utilizing creative group counselling was found to significantly decrease depression (p = .008), anxiety (p = .005), and stress (p = .012) whilst increasing self-compassion (p = .013). CONCLUSION: It is important for clinicians who care for single mothers, particularly if they recently immigrated, are multiparous, and an ethnic minority to encourage engagement in peer-initiated counselling and obtain mental health care as necessary. IMPACT: This study identified and addressed the mental health issues that single mothers face worldwide. This is also the first mixed methods research synthesis to report single mothers' ethnicity in nursing and midwifery literature. Thus, findings from this mixed methods research synthesis can help nurses worldwide build culturally-concordant programs in their respective community organizations and partners (e.g. community health centres, mother-child enrichment clubs), inform health policies, and promote safer spaces for many single mothers, particularly for those who will immigrate to the Global North (i.e. UK, US, Canada) and become an ethnic minority.

Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study.

Meta-analysis using individual participant data (IPD) is an important methodology in intervention research because it (a) increases accuracy and precision of estimates, (b) allows researchers to investigate mediators and moderators of treatment effects, and (c) makes use of extant data. IPD meta-analysis can be conducted either via a one-step approach that uses data from all studies simultaneously, or a two-step approach, which aggregates data for each study and then combines them in a traditional meta-analysis model. Unfortunately, there are no evidence-based guidelines for how best to approach IPD meta-analysis for count outcomes with many zeroes, such as alcohol use. We used simulation to compare the performance of four hurdle models (3 one-step and 1 two-step models) for zero-inflated count IPD, under realistic data conditions. Overall, all models yielded adequate coverage and bias for the treatment effect in the count portion of the model, across all data conditions. However, in the zero portion, the treatment effect was underestimated in most models and data conditions, especially when there were fewer studies. The performance of both one- and two-step approaches depended on the formulation of the treatment effects, suggesting a need to carefully consider model assumptions and specifications when using IPD.

Toward a holistic approach to EdTech effectiveness: Lessons from Covid-19 research in Bangladesh, Ghana, Kenya, Pakistan, and Sierra Leone.

During school closures prompted by the Covid-19 pandemic, educational technology (EdTech) was often used to continue educational provision. In this article, we consider EdTech effectiveness using a holistic framework, and synthesise findings from 10 primary research studies of EdTech interventions conducted in low- and middle-income countries during the pandemic. The framework includes five main lenses: learning outcomes, enhancing equity, implementation context, cost and affordability, and alignment and scale. While in-person schooling has largely resumed, there continues to be further integration of EdTech into education systems globally. This analysis provides evidence-based insights and highlights knowledge gaps to shape holistic analysis of both EdTech mainstreaming and future research into the effective use of EdTech to strengthen learning.

A systematic review and meta-analysis of intellectual, neuropsychological, and psychoeducational functioning in neurofibromatosis type 1.

Neurofibromatosis Type 1 (NF1) is a common genetic disorder frequently associated with cognitive deficits. Despite cognitive deficits being a key feature of NF1, the profile of such impairments in NF1 has been shown to be heterogeneous. Thus, we sought to quantitatively synthesize the extant literature on cognitive functioning in NF1. A random-effects meta-analysis of cross-sectional studies was carried out comparing cognitive functioning of patients with NF1 to typically developing or unaffected sibling comparison subjects of all ages. Analyses included 50 articles (Total NNF1 = 1,522; MAge = 15.70 years, range = 0.52-69.60), yielding 460 effect sizes. Overall moderate deficits were observed [g = -0.64, 95% CI = (-0.69, -0.60)] wherein impairments differed at the level of cognitive domain. Deficits ranged from large [general intelligence: g = -0.95, 95% CI = (-1.12, -0.79)] to small [emotion: g = -0.37, 95% CI = (-0.63, -0.11)]. Moderation analyses revealed nonsignificant contributions of age, sex, educational attainment, and parental level of education to outcomes. These results illustrate that cognitive impairments are diffuse and salient across the lifespan in NF1. Taken together, these results further demonstrate efforts should be made to evaluate and address cognitive morbidity in patients with NF1 in conjunction with existing best practices.

Urban biotic homogenization: Approaches and knowledge gaps.

Urbanization is restructuring ecosystems at an unprecedented pace, with complex and profound consequences for life on Earth. One of the hypothesized trajectories of urban ecosystems and species communities is biotic homogenization, possibly leading to very similar species assemblages in cities across the globe. Urbanization can, however, also have the opposite effect: biotic diversification, with cities, at least at the local scale, becoming biologically more diverse, mainly as a consequence of high species introduction rates and habitat diversification. Applying the hierarchy-of-hypotheses approach, we systematically map and structure the comprehensive body of literature on the urban biotic homogenization (UBH) hypothesis, comprising 225 individual studies (i.e., tests of the hypothesis) retrieved from 145 publications. The UBH hypothesis is studied at multiple levels with a multitude of approaches and underlying assumptions. We show that UBH is generally used with two very different connotations: about half of the studies investigated a potential increase in community similarity across cities, whereas the other half investigated biotic homogenization within cities, the latter being supported more frequently. We also found strong research biases: (1) a taxonomic bias towards birds and plants, (2) a bias towards small and medium distances (<5000 km) in comparisons across cities, (3) a dominance of studies substituting space for time versus true temporal studies, (4) a strong focus on terrestrial versus aquatic systems, (5) more extraurban (including periurban) areas than natural or rural ecosystems for comparison to urban systems, (6) a bias towards taxonomic versus functional, phylogenetic, and temporal homogenization, and (7) more studies undertaken in Europe and North America than in other continents. The overall level of empirical support for the UBH hypothesis was mixed, with 55% of the studies reporting supporting evidence. Results significantly differed when a natural/nature reserve, an extraurban, or rural/agricultural area served as reference to infer biotic homogenization, with homogenization being detected least frequently when urban systems were compared to agricultural, i.e., other anthropogenically influenced, study sites. We provide an evidence map and a bibliographic network and identify key references on UBH with the goal to enhance accessibility and orientation for future research on this topic.

A Systematic Review and Meta-Analysis of Takeover Performance During Conditionally Automated Driving.

OBJECTIVE: The aim of this paper was to synthesize the experimental research on factors that affect takeover performance during conditionally automated driving. BACKGROUND: For conditionally automated driving, the automated driving system (ADS) can handle the entire dynamic driving task but only for limited domains. When the system reaches a limit, the driver is responsible for taking over vehicle control, which may be affected by how much time they are provided to take over, what they were doing prior to the takeover, or the type of information provided to them during the takeover. METHOD: Out of 8446 articles identified by a systematic literature search, 48 articles containing 51 experiments were included in the meta-analysis. Coded independent variables were time budget, non-driving related task engagement and resource demands, and information support during the takeover. Coded dependent variables were takeover timing and quality measures. RESULTS: Engaging in non-driving related tasks results in degraded takeover performance, particularly if it has overlapping resource demands with the driving task. Weak evidence suggests takeover performance is impaired with shorter time budgets. Current implementations of information support did not affect takeover performance. CONCLUSION: Future research and implementation should focus on providing the driver more time to take over while automation is active and should further explore information support. APPLICATION: The results of the current paper indicate the need for the development and deployment of vehicle-to-everything (V2X) services and driver monitoring.

Positive but variable effects of crop diversification on biodiversity and ecosystem services.

Ecological theory suggests that biodiversity has a positive and stabilizing effect on the delivery of ecosystem services. Yet, the impacts of increasing the diversity of cultivated crop species or varieties in agroecosystems are still under scrutiny. The available empirical evidence is scattered in scope, agronomic and geographic contexts, and impacts on ecosystem services may depend on the type of diversification strategy used. To robustly assess the effects of crop diversification in agroecosystems, we compiled the results of 95 meta-analyses integrating 5156 experiments conducted over 84 experimental years and representing more than 54,500 paired observations on 120 crop species in 85 countries. Overall, our synthesis of experimental data from across the globe shows that crop diversification enhances not only crop production (median effect +14%) but also the associated biodiversity (+24%, i.e., the biodiversity of non-cultivated plants and animals), and several supporting and regulating ecosystem services including water quality (+51%), pest and disease control (+63%) and soil quality (+11%). However, there was substantial variability in the results for each individual ecosystem service between different diversification strategies such as agroforestry, intercropping, cover crops, crop rotation or variety mixtures. Agroforestry is particularly effective in delivering multiple ecosystem services, that is, water regulation and quality, pest and diseases regulation, associated biodiversity, long-term soil productivity and quality. Variety mixtures, instead, provide the lowest benefits, whereas the other strategies show intermediate results. Our results highlight that while increasing the diversity of cultivated crop species or varieties in agroecosystems represents a very promising strategy for more sustainable land management, contributing to enhanced yields, enhanced biodiversity and ecosystem services, some crop diversification strategies are more effective than others in supporting key ecosystem services.

Classifying human influences on terrestrial ecosystems.

Human activity is affecting every ecosystem on Earth, with terrestrial biodiversity decreasing rapidly. Human influences materialize in the form of numerous, jointly acting factors, yet the experimental study of such joint impacts is not well developed. We identify the absence of a systematic ordering system of factors according to their properties (traits) as an impediment to progress and offer an a priori trait-based factor classification to illustrate this point, starting at the coarsest level with the physical, biological or chemical nature of factors. Such factor classifications can serve in communication of science, but also can be used as heuristic tools to develop questions and formulate new hypotheses, or as predictors of effects, which we explore here. We hope that classifications such as the one proposed here can help shift the spotlight on the multitude of anthropogenic changes affecting ecosystems, and that such classifications can be used to help unravel joint impacts of a great number of factors.

Conducting proportional meta-analysis in different types of systematic reviews: a guide for synthesisers of evidence.

BACKGROUND: Single group data present unique challenges for synthesises of evidence. Proportional meta-analysis is becoming an increasingly common technique employed for the synthesis of single group data. Proportional meta-analysis shares many similarities with the conduct and reporting of comparative, or pairwise, meta-analysis. While robust and comprehensive methods exist detailing how researchers can conduct a meta-analysis that compares two (or more) groups against a common intervention, there is a scarcity of methodological guidance available to assist synthesisers of evidence in the conduct, interpretation, and importance of proportional meta-analysis in systematic reviews. MAIN BODY: This paper presents an overview targeted to synthesisers of evidence and systematic review authors that details the methods, importance, and interpretation of a proportional meta-analysis. We provide worked examples of how proportional meta-analyses have been conducted in research syntheses previously and consider the methods, statistical considerations, and presentation of this technique. CONCLUSION: This overview is designed to serve as practical guidance for synthesisers of evidence in the conduct of proportional meta-analyses.

[Sexual health information on social media: a systematic scoping review]. / Sexuelle Gesundheitsinformationen in sozialen Medien: Ein systematisches Scoping Review.

BACKGROUND: Information on sexual and reproductive health is increasingly disseminated via social media and reaches a large audience. RESEARCH AIM: Against this background, the aim of this paper is to systematically summarize the international state of research on sexual health information on social media for the first time with a scoping review. Seven research questions were explored, relating to the amount (F1), methods (F2: content analysis, F3: quality analysis), and results (F4: providers, F5: target groups, F6: topics, F7: quality of information) of previous studies. MATERIALS AND METHODS: The study follows the PRISMA framework for scoping reviews as well as the Open Science approach. It is preregistered, and all materials (codebook with reliability coefficients) and data (list of identified studies and coding of studies) are available on the Open Science Foundation server. RESULTS: A total of 69 studies with 72 data sets were identified, with more than half of the publications referring to YouTube (F1). Qualitative and quantitative methods of content analysis are used equally (F2) and quality analyses are rare (F3). Health laypersons dominate as the information providers (F4). The target groups are mostly unspecified (F5). The health information on social media examined in the previous studies covers a broad range of topics (F6). Where quality assessments were made, they tended to be negative (F7). DISCUSSION: More research is needed to better understand sexual and reproductive health information on social media and to promote its quality and constructive use.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis.

BACKGROUND: Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. METHODS: We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. RESULTS: We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features. CONCLUSIONS: The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.