How do psychobehavioural variables shed light on heterogeneity in COVID-19 vaccine acceptance? Evidence from United States general population surveys on a probability panel and social media.

Objectives: To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults' intention to get a COVID-19 vaccination, (2) identify segments of the population ('personas') who share similar factors predicting vaccination intention, (3) create a 'typing tool' to predict which persona people belong to and (4) track changes in the distribution of personas over time and across the USA.

Design: Three surveys: two on a probability-based household panel (NORC's AmeriSpeak) and one on Facebook.

Setting: The first two surveys were conducted in January 2021 and March 2021 when the COVID-19 vaccine had just been made available in the USA.

Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network.

Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021.

Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks.

Developing data-driven solutions that address real-world problems requires understanding of these problems' causes and how their interaction affects the outcome-often with only observational data. Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG). BNs could be especially useful for research in global health in Lower and Middle Income Countries, where there is an increasing abundance of observational data that could be harnessed for policy making, program evaluation, and intervention design.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

Background: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex.

Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.

Introduction: Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India.

Methods: Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613).

Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill?

There is a great need to develop new approaches for rehabilitation of the upper limb after stroke. Robotic therapy is a promising form of neurorehabilitation that can be delivered in higher doses than conventional therapy. Here we sought to determine whether the reported effects of robotic therapy, which have been based on clinical measures of impairment and function, are accompanied by improved motor control.

Persistent residual errors in motor adaptation tasks: reversion to baseline and exploratory escape.

When movements are perturbed in adaptation tasks, humans and other animals show incomplete compensation, tolerating small but sustained residual errors that persist despite repeated trials. State-space models explain this residual asymptotic error as interplay between learning from error and reversion to baseline, a form of forgetting. Previous work using zero-error-clamp trials has shown that reversion to baseline is not obligatory and can be overcome by manipulating feedback.

Overcoming motor "forgetting" through reinforcement of learned actions.

The human motor system rapidly adapts to systematic perturbations but the adapted behavior seems to be forgotten equally rapidly. The reason for this forgetting is unclear, as is how to overcome it to promote long-term learning. Here we show that adapted behavior can be stabilized by a period of binary feedback about success and failure in the absence of vector error feedback.

Improvement after constraint-induced movement therapy: recovery of normal motor control or task-specific compensation?

Background: Constraint-induced movement therapy (CIMT) has proven effective in increasing functional use of the affected arm in patients with chronic stroke. The mechanism of CIMT is not well understood.

Objective: To demonstrate, in a proof-of-concept study, the feasibility of using kinematic measures in conjunction with clinical outcome measures to better understand the mechanism of recovery in chronic stroke patients with mild to moderate motor impairments who undergo CIMT.

Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models.

Although motor learning is likely to involve multiple processes, phenomena observed in error-based motor learning paradigms tend to be conceptualized in terms of only a single process: adaptation, which occurs through updating an internal model. Here we argue that fundamental phenomena like movement direction biases, savings (faster relearning), and interference do not relate to adaptation but instead are attributable to two additional learning processes that can be characterized as model-free: use-dependent plasticity and operant reinforcement. Although usually "hidden" behind adaptation, we demonstrate, with modified visuomotor rotation paradigms, that these distinct model-based and model-free processes combine to learn an error-based motor task.

Persistence of motor memories reflects statistics of the learning event.

Learning to control a new tool (i.e., a novel environment) produces an internal model, i.

Robotic neurorehabilitation: a computational motor learning perspective.

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients.

Active learning: learning a motor skill without a coach.

When we learn a new skill (e.g., golf) without a coach, we are "active learners": we have to choose the specific components of the task on which to train (e.

Evolution of motor memory during the seconds after observation of motor error.

When a movement results in error, the nervous system amends the motor commands that generate the subsequent movement. Here we show that this adaptation depends not just on error, but also on passage of time between the two movements. We observed that subjects learned a reaching task faster, i.