Publications by authors named "R Todaro"

Background: Parkinson's disease (PD) is a complex neurodegenerative disorder impacting everyday function and quality of life. Rehabilitation plays a crucial role in improving symptoms, function, and quality of life and reducing disability, particularly given the lack of disease-modifying agents and limitations of medications and surgical therapies. However, rehabilitative care is under-recognized and under-utilized in PD and often only utilized in later disease stages, despite research and guidelines demonstrating its positive effects.

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Background: The number of inpatient mental health facilities for children and adolescents in the United States is growing rapidly. While undergoing inpatient treatment, children and adolescents can benefit from innovative play opportunities designed to foster social interaction and learning.

Methods: The Playful Learning Landscapes (PLL) initiative is a group of projects designed to transform everyday spaces into opportunities for playful learning.

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What if the environment could be transformed in culturally-responsive and inclusive ways to foster high-quality interactions and spark conversations that drive learning? In this article, we describe a new initiative accomplishing this, called Playful Learning Landscapes (PLL). PLL is an evidence-based initiative that blends findings from the science of learning with community-based participatory research to transform physical public spaces and educational settings into playful learning hubs. Here, we describe our model for conducting this research, which is mindful of three key components: community input, children learn best, and children need to learn to be successful in the 21st century economy.

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Predictions begin with an extrapolation of the properties of their underlying representations to forecast a future state not presently in evidence. For numerical predictions, sets of numbers are summarized and the result forms the basis of and constrains numerical predictions. One open question is how the accuracy of underlying representations influences predictions, particularly numerical predictions.

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