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Nurses’ Experiences with responsive behaviours in a complex dementia care unit: an interpretative descriptive study
No full textBackground: Dementia is a progressive condition that affects cognition, judgment, and orientation. In advanced stages, these changes often manifest as responsive behaviors such as agitation, aggression, and wandering, which usually reflect unmet needs that individuals living with dementia are unable to communicate (Dupuis et al., 2012). Person-centered care and non-pharmacological interventions are widely recommended, yet managing responsive behaviors remains a significant challenge for nurses. In complex dementia care (CDC) units, such behaviors are particularly common given individual’s clinical profiles. Despite ongoing education and training, a gap persists between nurses’ understanding of responsive behaviors and their ability to consistently apply best-practice interventions (Kong et al., 2021). This gap highlights the need to explore nurses’ experiences to better understand how they manage these behaviors in daily practice. Aim: This study aimed to explore how nurses experience and manage responsive behaviors in a CDC setting in Alberta, Canada. Methods: Qualitative design was employed using Thorne’s Interpretive Description methodology. In-person, semi-structured interviews were conducted with seven English-speaking nurses who had received dementia education and were working in a CDC unit. Data were analyzed using Braun and Clarke’s (2021) experiential thematic analysis approach, allowing patterns of meaning to emerge from participants’ narratives. Findings: Three key themes emerged. First, “Don’t Expect Any Miracles” highlighted how nurses reframed behaviors as “expressions” of unmet needs and emphasized the importance of not taking them personally. The second theme, “Big Toolkit,” reflected diverse strategies, where nurses acted as detectives to uncover causes, relied on individuals’ histories, and adapted approaches with flexibility, supported by ongoing education. The third theme, “Challenges and Barriers to Effective Care,” was evident at multiple levels: nurse-level barriers included varying competence and emotional strain; organizational barriers encompassed staffing shortages, workload pressures, and crowded layouts; and resident-level barriers involved advanced dementia, complex needs, and difficulties in truly knowing the person Conclusion: Nurses’ narratives reflected the complexity and emotional intensity of working in CDC units. Managing responsive behaviors requires individualized, team-driven, and primarily non-pharmacological approaches. Enhancing staff education, strengthening interdisciplinary collaboration and ensuring sustained organizational commitment are essential to improving both residents’ quality of life and staff support
SparseEMG: A Computational Framework to Design Sparse Electrode Layouts for Sensing Gestures
No full textGesture recognition with electromyography (EMG) is a complex problem influenced by gesture sets, electrode count and placement, and machine learning parameters (e.g., features, classifiers). Most existing toolkits focus on streamlining model development but overlook the impact of electrode selection on classification accuracy. In this work, we present the first data-driven analysis of how electrode selection and classifier choice affect both accuracy and sparsity. Through a systematic evaluation of 28 combina-tions (4 selection schemes, 7 classifiers), across six datasets, we identify an approach that minimizes electrode count without compromising accu-racy. The results show that Permutation Importance (selection scheme) with Random Forest (classifier) reduces the number of electrodes by 53.5%. Based on these findings, we introduce SparseEMG, a design tool that gen-erates sparse electrode layouts based on user-selected gesture sets, elec-trode constraints, and ML parameters while also predicting classification performance. SparseEMG supports 50+ unique gestures and is validated in three real-world applications using different hardware setups. Results from our multi-dataset evaluation show that the layouts generated from the SparseEMG design tool are transferable across users with only minimal variation in gesture recognition performance
Multiaxial Ultrasound Transducers for Medical Ultrasound imaging
No full textMedical ultrasound imaging is one of the most popular diagnostic imaging methods worldwide. Recent years have seen renewed interest in applications such as passive ultrasound imaging (for monitoring ultrasound therapies), and ultrasound computer tomography (for high-fidelity diagnostic imaging). Underpinning all of these systems are piezoelectric ultrasound transducers, which convert between mechanical and electrical energy. Current applications of ultrasound transducers exploit the piezoelectric effect in only one direction, which allows them to either generate a fixed ultrasound profile or measure incident acoustic pressure. In imaging, the measurement of only pressure introduces limitations and necessary assumptions to image reconstruction methods that can introduce artifacts. While larger arrays of transducers can mitigate artifacts, this also increases cost and computational expense. Recent developments, however, have introduced multiaxial driving, a technique that generates a steerable ultrasound emission profile by exploiting the piezoelectric effect in two or more axial directions. Preliminary work also demonstrated that this process is reversible: the two or more electrical signals measured from a multiaxial transducer vary according to the Direction of Arrival (DOA) of the wave that generated them. This observation motivated the following objectives of this research: 1) The characterization of measured multiaxial signals relative to a wave's DOA, 2) The demonstration of a biaxial method for passive ultrasound imaging, 3) The demonstration of a biaxial method for Ultrasound Computer Tomography (UCT) For objective 1, finite-element simulations and experimental transducers were used to demonstrate DOA estimation over a range of 48 degrees using the phase difference and amplitude ratio between the two measured biaxial signals. For objective 2, we provided the first demonstration of a single-element passive beamforming algorithm and passive ultrasound imaging without conventional time-of-flight information using an experimental, two-element biaxial sensor array. Finally, for objective 3, we provided the first demonstration of a bent-ray UCT algorithm that uses only DOA information. A simulated 128-sensor array showed improved image sharpness and structural quality compared to conventional methods. Overall, this dissertation demonstrates the utility of multiaxial ultrasound sensors, the novel information they provide, and offers two robust applications that improve on conventional ultrasound imaging methods, enabling new approaches for future systems
Rapid feedback responses when planning, performing and terminating goal-directed reaching movements
No full textThe healthy nervous system transitions seamlessly between actions with different goals. A tennis player, for example, waits in a ready position before lunging sideways to return a serve. This simple action involves disengaging from posture to perform the movement and then stabilizing again after contact to prepare for the next return. These transitions occur seamlessly despite evidence that the neural control supporting these goals may be distinct. We know little about how neural control changes when transitioning between these distinct goals. Here, we examined how healthy participants corrected their movements when disturbed by mechanical perturbations (n = 40; 20 females) while planning, performing, and terminating a movement (Experiment 1). Hand displacement and muscle activity were measured to quantify feedback responses and assess changes in neural control. We found that participants were displaced less and generated larger muscle responses when disturbed during movement compared to when holding posture prior to movement or after reengaging posture control in the goal target. Additionally, peak displacements were smaller, and muscle responses were larger when reengaging posture control in the goal target compared to holding posture during the planning phase before movement. The contribution of each muscle varied across the phases of movement and was shaped by the level of activity when the disturbance occurred. Pre-existing muscle activity impacted the responsiveness of individual muscles. Therefore, we added a background load to observe how it altered the amplitude and scaling of corrective responses. Participants countered a background load that excited the elbow extensor muscles (Experiment 2). They also encountered the same perturbations as Experiment 1. When the background load was applied, it reduced peak displacements and increased the amplitude of muscle responses, particularly when the loaded muscle was stretched by the perturbation. Indeed, the background load increased sensitivity to proprioceptive information, especially when the loaded muscle was stretched by the perturbation. Overall, our findings demonstrate that the nervous system alters its neural control when engaging in movement from posture (and vice versa). The nervous system is more sensitive to sensory feedback when moving and the least sensitive to the same perturbations when maintaining posture before movement
Mindfulness as a Buffer Against Stress: A Moderation Analysis of University Student Psychological Well-Being
No full textUniversity students frequently experience elevated stress that negatively impacts psychological well-being. Excessive stress acts as a barrier to well-being, academic success, and overall quality of life for university students, which can pose ongoing challenges for universities in supporting students effectively. Mindfulness, which is typically defined as present-moment, nonjudgmental awareness, has been proposed as a potential protective factor. Although prior research links mindfulness to lower stress and increased well-being, its buffering role between stress and psychological well-being remains unclear in undergraduate populations. Addressing this gap is important for informing evidence-based interventions aimed at promoting resilience and retention in higher education. This cross-sectional survey examined whether trait attentional awareness (i.e., a foundational component of mindfulness) moderated the relationship between perceived stress and psychological well-being among University of Calgary undergraduate students (N = 237). Participants completed the Mindful Attention Awareness Scale (MAAS), the Perceived Stress Scale (PSS), and the 18-item Psychological Well-Being Scale (PWBS). Due to poor reliability and inadequate fit of the theorized six-factor PWBS structure, exploratory factor analysis supported a two-factor solution representing hedonic and eudaimonic well-being. These factors were used as outcomes in two moderated regression models. Results indicated that perceived stress was a significant negative predictor of both forms of well-being, whereas attentional awareness demonstrated a small positive association with hedonic well-being only. Attentional awareness did not moderate the stress–well-being relationship for either outcome. Overall, the findings indicate that attentional awareness, as operationalized in the present study, was associated with well-being but did not function as a stress-buffering factor in this undergraduate sample. Results highlight the need for multidimensional mindfulness measures and more psychometrically robust well-being instruments in future research
Heart Failure in Kidney Transplant Recipients: Narrative Review of Risk Factors, Therapy, and Current Gaps in Management
No full textAbstract Among kidney transplant recipients (KTR), cardiovascular disease remains the leading cause of mortality, with heart failure (HF) being especially common in this population. Risk factors for HF in KTR can be categorized as traditional (risks relevant to the general population) versus nontraditional (unique to kidney disease and transplantation). Despite the substantial burden of cardiovascular disease, KTR have been excluded from large clinical trials in cardiovascular medicine and from landmark studies looking at the kidney-protective effects of novel cardiorenal metabolic agents, including sodium–glucose cotransporter 2 inhibitors (SGLT2i). Thus, there is uncertainty about the safety and efficacy of the use of guideline directed medical therapy (GDMT) for HF in this population. Long-term optimal use of GDMT in KTR is limited by various barriers, including the risk of hyperkalemia from concomitant use of renin–angiotensin inhibitors and mineralocorticoid receptor antagonists, fears about genitourinary infections from SGLT2i, and lack of evidence for kidney-protective effects from GDMT agents among KTR. Overcoming these barriers will require a multifaceted approach that involves evidence generation to understand the efficacy and safety of GDMT among KTR, implementation-based strategies to increase uptake of GDMT, and development of multispecialty combined clinical care approaches to care for KTR with HF
Mild Behavioural Impairment-Apathy as an Early Behavioural Marker of Alzheimer Disease
No full textNeuropsychiatric symptoms (NPS) may signal the earliest clinical expression of neurodegenerative disease in some individuals, even before cognitive impairment. Mild behavioural impairment (MBI) is a validated syndrome that captures emergent, persistent behavioural changes in older adults without dementia and identifies individuals at elevated risk for cognitive decline and incident dementia. Among MBI domains, apathy, marked by diminished interest, initiative, and emotional reactivity, is particularly relevant given the high prevalence of apathy in neurodegenerative disease. This thesis investigates MBI-apathy across clinical, biomarker, and neuroimaging perspectives to clarify the role of MBI-apathy as an early marker of Alzheimer Disease (AD) in cognitively normal individuals and those with mild cognitive impairment. Using longitudinal data from the National Alzheimer’s Coordinating Center, the first study showed that individuals with MBI-apathy had a significantly greater rate of incident dementia, with most cases progressing specifically to AD dementia. In the AD Neuroimaging Initiative, the second study demonstrated associations between MBI-apathy and higher cerebrospinal fluid phosphorylated tau (p-tau)181/Aβ42 and total tau (t-tau)/Aβ42 ratios, and higher p-tau181 levels cross-sectionally and over two years. The third study extended these findings, showing that MBI-apathy was associated with higher plasma p-tau181 levels cross-sectionally and over two and three years. The last study demonstrated that MBI-apathy was associated with lower cortical thickness and grey matter volume in AD-related meta-regions of interest, as well as in prefrontal regions implicated in motivation, including the dorsolateral prefrontal cortex and orbitofrontal cortex. Across modalities, findings position MBI-apathy as a behavioural marker of AD pathology and neurodegeneration, supporting a broader conceptualization of preclinical and prodromal AD that recognizes behavioural change as an integral component of early disease expression
Statistical Methods for Integrative Inference with Imperfect and Heterogeneous Data Sources
No full textProbability samples serve as a foundation for unbiased statistical inference but are often costly and prone to incomplete information, whereas non-probability samples are efficient and convenient but lack a unified framework for valid inference. This thesis develops a series of statistical methodologies for estimating the population mean of a response variable by integrating probability and non-probability samples across a range of imperfect-data settings commonly encountered in modern observational studies and survey research. Chapter 2 introduces a flexible likelihood-based framework for integrating a probability sample with fully observed covariates and a non-probability sample with a misclassified response measured through multiple surrogates. The proposed inference procedure enables consistent estimation of the population mean and improves efficiency by leveraging auxiliary information from the probability sample. Chapter 3 adapts the framework to settings where a common covariate is misclassified in both the probability and non-probability samples and measured through multiple sur-rogates. A likelihood-based estimator and a doubly robust (DR) extension are developed to integrate a probability sample with surrogate-measured covariate and no response and a non-probability sample with the same surrogate-measured covariate and fully observed response, with a shared set of fully observed auxiliary covariates in both samples, yielding consistent population mean estimation under partial model misspecification. Chapter 4 further considers joint misclassification of both a covariate and the response. A joint likelihood-based procedure is proposed to simultaneously recover the latent covari-ate and response by integrating a probability sample in which the covariate is misclassi-fied through multiple surrogates, other covariates are fully and precisely observed, and the response is unavailable, with a non-probability sample that contains the same surrogate-measured covariate, the same set of fully observed auxiliary covariates, and a response that is itself misclassified via multiple surrogates. The proposed integration strategy corrects for selection bias and achieves consistent population mean estimation under correct outcome model specification, with substantial gains in bias reduction and efficiency. Chapter 5 extends the framework to survival analysis by integrating a probability sample with complete covariates and no response and a non-probability sample with a right-censored time-to-event outcome. Parametric regression-based, inverse probability weighting (IPW), and doubly robust estimators are developed to estimate the population mean event time, with the DR estimator exhibiting strong robustness in simulation studies. Collectively, this thesis provides a unified likelihood-based framework for integrating probability and non-probability samples under various forms of data imperfection, advances population-level statistical inference in the presence of misclassification, missingness, and biased sampling mechanisms, and identifies promising directions for future methodological development in more complex data settings
The Story of ii’ taa’poh’to’p: University of Calgary’s Journey Towards an Indigenous Strategy
No full textThe University of Calgary’s Indigenous strategy, ii’taa’poh’to’p, lays the path for a journey of transformation and renewal for truth and reconciliation through ways of knowing, doing, connecting, and being.
The Story of ii’ taa’poh’to’p is the story of the creation of the University of Calgary’s Indigenous Strategy. The result of an enlightening process of relationship building and deep learning and listening, it required the intentional and careful creation of parallel paths for institutional and Indigenous frameworks to create the strategy. Authentic conversations occurred in the ethical space between the parallel paths, allowing for increased understanding of differences and similarities between cultures.
This book captures powerful and emotional stories that emphasize the importance of reconciliation and decolonizing organizations. It demonstrates that trusting relationships can be developed between Indigenous and non-Indigenous relatives and lays out a dynamic framework and approach for the development of an Indigenous strategy.
The Grandparents of ii’ taa’poh’to’p welcome readers to learn from their experience. They share insightful lessons about the importance of being relational; honouring ways of knowing and doing from other cultures; developing generational strategies that persist over time; understanding the impacts of fear; and making assumptions about people’s prior knowledge. They discuss how relationship building through deep listening across cultures is essential to the development of an Indigenous strategy. The Story of ii’ taa’poh’to’p is essential reading for all those interested in the development of an Indigenous strategy in the pursuit of truth and reconciliation
A Trust Model for Human-Machine Systems under Mutual Influence of Human and Machine Reliability: Virtual Reality Use-cases
No full textHuman trust in machines plays a pivotal role in performing a task effectively within human–machine systems (HMS). Prior work defined a three-layered framework for such trust, encasing all three elements of HMS – human, machine, and environment, but also indicated two deficiencies. Firstly, there is an absence of a model with metrics spanning all layers to objectively measure fluctuations of the trust (trust dynamics) in real time. Secondly, there is an inadequate consideration of human and machine reliability for evaluating task performance and the trust. Herein, the aim of this thesis is to propose and evaluate a trust model with metrics spanning all three layers to objectively measure trust dynamics. To achieve this aim, two challenges must be addressed: (1) assessing the mutual influence of human and machine reliability on task performance and (2) formulating the metrics based on objective measurement of behavioral data. By addressing these challenges, this thesis makes three contributions: (a) developing a trust model with metrics aligned with the three-layered framework and formulated to be objectively measurable; (b) enabling concurrent consideration of human and machine reliability within HMS; and (c) investigating the mutual influence of human and machine reliability on task performance and applying these findings in the model’s empirical evaluation across two virtual reality (VR) use-cases. The outcomes of the evaluation demonstrate the pertinence of the model in measuring trust dynamics while accounting for both human and machine reliability. And the model’s objective measurement was notably sensitive to trust dynamics than its subjective counterpart. The proposed model could enable designing trustworthy and adaptive HMS for applications across various domains such as autonomous vehicles, telesurgery, aviation, and immersive VR training