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The use of observational tools in psychological assessment has decreased in recent years, mainly due to its personnel and time costs, and researchers have not explored methodological innovations like adaptive algorithms in observational assessment. In the present study, we introduce the behavior-driven observation procedure to develop, test, and implement observational adaptive instruments. In Study 1, we use a preexisting observational checklist to evaluate nonverbal behaviors related to psychotic symptoms and to specify the adaptive algorithm’s model. We fit the model to observational data collected from 114 participants. The results support the model’s goodness of fit. In Study 2, we use the estimated model parameters to calibrate the adaptive procedure and test the algorithm for accuracy and efficiency in adaptively reconstructing 58 nonadaptively collected response patterns. The results show the algorithm’s good accuracy and efficiency, with a 40% average reduction in the number of administered items. In Study 3, we used real raters to test the adaptive checklist built with behavior-driven observation. The results indicate adequate intrarater agreement and good consistency of the observed response patterns. In conclusion, the results support the possibility of using behavior-driven observation to create accurate and affordable (in terms of resources) observational assessment tools.

Systematic reviews and meta-analyses pool data from individual studies to generate a higher level of evidence to be evaluated by guidelines. These reviews ultimately guide clinicians and stakeholders in health-related decisions. However, the informativeness and quality of evidence synthesis inherently depend on the quality of what has been pooled into meta-research projects. Moreover, beyond the quality of included individual studies, only a methodologically correct process, in relation to systematic reviews and meta-analyses themselves, can produce a reliable and valid evidence synthesis. Hence, quality of meta-research projects also affects evidence synthesis reliability. In this overview, the authors provide a synthesis of advantages and disadvantages and main characteristics of some of the most frequently used tools to assess quality of individual studies, systematic reviews, and meta-analyses. Specifically, the tools considered in this work are the Newcastle-Ottawa scale (NOS) and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for observational studies, the Consolidated Standards of Reporting Trials (CONSORT), the Jadad scale, the Cochrane risk of bias tool 2 (RoB2) for randomized controlled trials, the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) and the Assessment of Multiple Systematic Reviews 2 (AMSTAR2), and AMSTAR-PLUS for meta-analyses. What is already known?: The informativeness and quality of evidence synthesis inherently depend on the quality of what has been pooled into meta-research projects. Beyond the quality of included individual studies, only a methodologically correct process, in relation to systematic reviews and meta-analyses themselves, can produce a reliable and valid evidence synthesis. What is new?: In this overview, the authors provide a synthesis of advantages and disadvantages and main characteristics of some of the most frequently used tools to assess quality of individual studies, systematic reviews, and meta-analyses. Potential impact: This overview serves as a starting point and a brief guide to identify and understand the main and most frequently used tools for assessing the quality of studies included in meta-research. The authors here share their experience in publishing several meta-research-related articles covering different areas of medical sciences. © 2020 John Wiley & Sons Ltd