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.
METHODS: 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.
METHODS: 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.
METHODS: Data were extracted using a predesigned data extraction form and narratively synthesized.
RESULTS: Twenty reviews met our inclusion criteria reporting on outcomes related to students, the public, curriculum and future professional practice.
CONCLUSIONS: 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.
UNASSIGNED: 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.

BACKGROUND: Recovery strategies are used to enhance performance and reduce injury risk in athletes. In previous systematic reviews, individual recovery strategies were investigated to clarify their effectiveness for mixed groups of athletes. However, the current evidence is ambiguous, and a clear overview of (training) recovery for endurance athletes is still lacking.
METHODS: We conducted an umbrella review based on a literature search in PubMed, Cochrane Database of Systematic Reviews, and Web of Science. Reviews published in English and before December 2022 were included. Systematic reviews and meta-analyses were eligible if they investigated the effectiveness of one or more recovery strategies compared with a placebo or control group after a training session in endurance athletes.
RESULTS: Twenty-two reviews (nine systematic reviews, three meta-analyses, and ten systematic reviews with meta-analyses included) met the inclusion criteria. In total, sixty-three studies with 1100 endurance athletes were included in our umbrella review. Out of the sixty-three studies, eight provided information on training recovery time frame for data synthesis. Among them, cryotherapy and compression garments showed positive effects, while applying massage showed no effect. In general, none of the included recovery strategies showed consistent benefits for endurance athletes.
CONCLUSIONS: There is no particular recovery strategy that can be advised to enhance recovery between training sessions or competitions in endurance athletes. However, individual studies suggest that compression garments and cryotherapy are effective training recovery strategies. Further research should improve methodology and focus on the different time courses of the recovery process.
BACKGROUND: The review protocol was registered with the International Prospective Register of Systematic Reviews with the number CRD42021260509.

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.

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.

The rise of research synthesis and systematic reviews over the last 25 years has been aided by a series of software packages providing simple and accessible GUI interfaces which are intuitively easy to use by novice analysts and users. Development of many of these packages has been abandoned over time due to a variety of factors, leaving a gap in the software infrastructure available for meta-analysis. To fulfill the continued demand for a GUI-based meta-analytic system, we have now released MetaWin 3 as free, open-source, multi-platform software. MetaWin3 is written in Python and developed from scratch relative to earlier versions. The codebase is available on Github, with pre-compiled executables for both Windows and macOS available from the MetaWin website. MetaWin includes standardized effect size calculations, exploratory and publication bias analyses, and allows for both simple and complex explanatory models of variation within a meta-analytic framework, including meta-regression, using traditional least-squares/moments estimation.

This chapter begins with an overview of recent developments that have encouraged and facilitated greater use of research syntheses, including Meta-Analysis, to guide public policy and practice in education, workforce development, and social services. It discusses the role of Meta-Analysis for improving knowledge of the effectiveness of programs, policies, and practices and the applicability and generalizability of that knowledge to conditions other than those represented by the study samples and settings. The chapter concludes with recommendations for improving the potential of Meta-Analysis to accelerate knowledge development through changing how we design, conduct, and report findings of individual studies to maximize their usefulness in Meta-Analysis as well as how we produce and report Meta-Analysis findings. The paper includes references to resources supporting the recommendations.

Psoriasis is a waxing and waning skin disorder, often associated with a plethora of co-morbidities, including psoriatic arthritis (PsA), a severe form of chronic inflammatory arthritis. All forms of psoriasis and PsA are immune-mediated diseases where the patient\'s immune system is overactive in the production of certain factors that stimulate and activate the function of certain immune cells. Recent evidence has uncovered an important role for cell-mediated immunity in the aetiology and course of psoriasis and PsA, with a critical role played by the pro-inflammatory IL-23/TH17 axis. Taken together, these new lines of evidence suggest new and improved therapeutic interventions for patients with psoriasis and PsA. The hypothesis-driven process of inquiry of the best available evidence and its implication, application and evaluation in the context of clinical practice pertains to the meta-science of evidence-based health care (EBHC). EBHC consists in the initial step of research synthesis and generation of the systematic review of the best available evidence, estimated both qualitatively and quantitatively (i.e., meta-analysis). Evidence-based decision-making, a process driven and controlled by the expertise of the clinician and by the clinical needs and personal wants of the patient, is the principal, most timely and critical aspect of evidence-based practice. Recent and systematic reviews for the treatment of psoriasis and PsA consistently updated for emerging new and revised data (i.e., living systematic reviews) confirm the efficacy and the effectiveness of methotrexate (MTX) in containing and controlling psoriasis. The outcomes of MTX intervention for PsA remain mixed and inconclusive.

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\'.

UNASSIGNED: Tannins are a group of phenolic compounds that can modify the rumen biohydrogenation (BH) of polyunsaturated fatty acids (PUFA), but to date results obtained have been inconsistent. This study therefore aims to conduct a meta-analysis of the scientific literature related to the effects of tannins on rumen BH and fermentation.
UNASSIGNED: A total of 28 articles were collected from various scientific databases, such as Scopus, Science Direct and Google Scholar, and the data were analysed using a random effects model and meta-regression for rumen BH. The publication bias on the main variables of rumen fermentation was assessed using a funnel plot and Egger\'s test.
UNASSIGNED: An increase in tannin levels significantly reduced methane production (p < 0.001) and the population of Butyrivibrio fibrisolvens (p < 0.05). Dietary tannins also decreased the SFA proportion (p < 0.001) and increased (p < 0.001) the rumen monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) proportions. In additions, there were negative relationships between dietary tannin levels and BH rates of C18:2 n-6 and C18:3 n-3 (p < 0.05).
UNASSIGNED: Dietary tannins modulate the rumen fermentation profile, mitigate methane emissions, and inhibit rumen BH of PUFA.

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.