Underrepresentation of people from racial and ethnic minoritized groups in clinical trials threatens external validity of clinical and translational science, diminishes uptake of innovations into practice, and restricts access to the potential benefits of participation. Despite efforts to increase diversity in clinical trials, children and adults from Latino backgrounds remain underrepresented. Quality improvement concepts, strategies, and tools demonstrate promise in enhancing recruitment and enrollment in clinical trials. To demonstrate this promise, we draw upon our team’s experience conducting a randomized clinical trial that tests three behavioral interventions designed to promote equity in language and social-emotional skill acquisition among Latino parent–infant dyads from under-resourced communities. The recruitment activities took place during the COVID-19 pandemic, which intensified the need for responsive strategies and procedures. We used the Model for Improvement to achieve our recruitment goals. Across study stages, we engaged strategies such as (1) intentional team formation, (2) participatory approaches to setting goals, monitoring achievement, selecting change strategies, and (3) small iterative tests that informed additional efforts. These strategies helped our team overcome several barriers. These strategies may help other researchers apply quality improvement tools to increase participation in clinical and translational research among people from minoritized groups.

Long-duration gamma-ray burst (GRB) afterglow observations offer cutting-edge opportunities to characterise the star formation history of the Universe back to the epoch of reionisation, and to measure the chemical composition of interstellar and intergalactic gas through absorption spectroscopy. The main barrier to progress is the low efficiency in rapidly and confidently identifying which bursts are high redshift ( $z > 5$ ) candidates before they fade, as this requires low-latency follow-up observations at near-infrared wavelengths (or longer) to determine a reliable photometric redshift estimate. Since no current or planned gamma-ray observatories carry near-infrared telescopes on-board, complementary facilities are needed. So far this task has been performed by instruments on the ground, but sky visibility and weather constraints limit the number of GRB targets that can be observed and the speed at which follow-up is possible. In this work we develop a Monte Carlo simulation framework to investigate an alternative approach based on the use of a rapid-response near-infrared nano-satellite, capable of simultaneous imaging in four bands from $0.8$ to $1.7\,\unicode{x03BC}$ m (a mission concept called SkyHopper). Using as reference a sample of 88 afterglows observed with the GROND instrument on the MPG/ESO telescope, we find that such a nano-satellite is capable of detecting in the H-band (1.6 $\unicode{x03BC}$ m) $72.5\% \pm 3.1\%$ of GRBs concurrently observable with the Swift satellite via its UVOT instrument (and $44.1\% \pm 12.3\%$ of high redshift ( $z>5$ ) GRBs) within 60 min of the GRB prompt emission. This corresponds to detecting ${\sim}55$ GRB afterglows per year, of which 1–3 have $z > 5$ . These rates represent a substantial contribution to the field of high-z GRB science, as only 23 $z > 5$ GRBs have been collectively discovered by the entire astronomical community over the last ${\sim}24$ yr. Future discoveries are critically needed to take advantage of next generation follow-up spectroscopic facilities such as 30m-class ground telescopes and the James Webb Space Telescope. Furthermore, a systematic space-based follow-up of afterglows in the near-infrared will offer new insight on the population of dusty (‘dark’) GRBs which are primarily found at cosmic noon ( $z\sim 1-3$ ). Additionally, we find that launching a mini-constellation of 3 near-infrared nano-satellites would increase the detection fraction of afterglows to ${\sim}83\%$ and substantially reduce the latency in the photometric redshift determination.

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

This review includes a brief discussion, from the perspective of the pediatric cardiologist, of the rationale for creation and maintenance of multi-institutional databases of outcomes of the treatment of patients with congenital and paediatric cardiac disease, together with a history of the evolution of such databases, and a description of the current state of the art. A number of projects designed to have broad-based impact are currently in the design phase, or have already been implemented. Not surprisingly, most of the efforts thus far have focused on catheterization procedures and interventions, although some work examining other aspects of paediatric cardiology practice is also beginning. This review briefly describes several European and North American initiatives related to databases for pediatric and congenital cardiology including the Central Cardiac Audit Database of the United Kingdom, national database initiatives for pediatric cardiology in Switzerland and Germany, various database initiatives under the leadership of the Working Groups of The Association for European Paediatric Cardiology, the IMPACT Registry™ (IMproving Pediatric and Adult Congenital Treatment) of the National Cardiovascular Data Registry® of The American College of Cardiology Foundation® and The Society for Cardiovascular Angiography and Interventions (SCAI), the Mid-Atlantic Group of Interventional Cardiology (MAGIC) Catheterization Outcomes Project, the Congenital Cardiac Catheterization Project on Outcomes (C3PO), the Congenital Cardiovascular Interventional Study Consortium (CCISC), and the Joint Council on Congenital Heart Disease (JCCHD) National Quality Improvement Initiative. These projects, each leveraging multicentre data and collaboration, demonstrate the enormous progress that has occurred over the last several years to improve the quality and consistency of information about nonsurgical treatment for congenital cardiac disease. The paediatric cardiology field is well-poised to move quickly beyond outcome assessment and benchmarking, to collaborative quality improvement.