BEYOND WORDS: HOW LEARNING TO READ AND MENTAL HEALTH ARE RELATED FOR STRUGGLING READERS

The overarching purpose of this research program was to describe how intervening for academic deficits may be accompanied by changes in mental health. This multi-dimensional, multi-perspective, and iterative research program was developed to report on two distinct but related studies that addressed the same issue: in what ways does the mental health of students change as they transition from being struggling readers to more able readers? To describe the changes, these studies used a number of qualitative research methodologies—focus groups, individual interviews, and ethnographic case studies. Themes that emerged from the focus group and interview data in the first study were used to create a model that guided observations and interview questions in the second study. The first study described what parents, classroom teachers, and two reading instructors of nine previously struggling readers reported as the outcomes of becoming a more proficient reader. Data from this study indicated three broad domains in which change, as perceived by participants, occurred―cognitive/learning, behavioural/social, and psychological/emotional. Within these three domains, six dimensions were identified as having changed as reading improved: (a) academic achievement, (b) attitude, (c) attention, (d) behaviour, (e) mental health, and (f) empowerment. These domains, dimensions, and 15 constituent elements were used to create the model to guide the subsequent study. The purpose of the second study was to validate and refine this model by using an ethnographic case study approach to explore the ways in which the model accounted for the changes in reading and mental health seen in three boys over the months they participated in the intervention. By investigating the relationship between learning to read and mental health, this research aimed to enhance our understanding of how gains in reading may also improve the mental health of struggling readers. The model was found to be robust and a convenient conceptual framework to further our understanding of this relationship. Importantly, gains made in the cognitive/learning domain through an effective reading intervention, offered in a supportive learning environment, were shown to be accompanied by concomitant gains in both the behavioural/social and psychological/emotional domains—all of which enhance student thriving.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.