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Parent Anxiety and Child Psychopathology: A Longitudinal Study of Children with Physical Illness
No full textEstimates suggest that one in four children live with a chronic physical illness (CPI), such as arthritis, asthma, and diabetes. Children with CPI are vulnerable to developing psychopathology, known as multimorbidity. Approximately 40% of children with CPI experience multimorbidity, which is associated with lower self-esteem, poorer health-related quality of life, and a greater risk of substance use and suicidality. These negative outcomes may be a consequence of the substantial illness-related stress and uncertainty experienced by children with CPI and their families. To mitigate the compounding effects of child multimorbidity, it is essential to prioritize the health of these children by providing comprehensive, evidence-based healthcare services. However, doing so requires a deeper understanding of child multimorbidity, particularly in the family context. Beyond the affected child, parents of these children are likely to experience greater anxiety, resulting from managing CPI-related practical and emotional strains. This added stress and anxiety experienced by caregiving parents is posited to strengthen the association between parent and child mental health. Indeed, parent psychopathology has been identified as a key determinant of child multimorbidity; thus, efforts to improve the mental health of parents, in addition to the child, are paramount.
Although there is consensus in the literature regarding the risk of children with CPI to developing multimorbidity, several knowledge gaps remain. Studies on child multimorbidity are predominantly illness specific, cross-sectional, and have short follow-up periods. Moreover, few studies have examined anxiety trajectories among parents of children with CPI, and the intersection between parent anxiety and child psychopathology. Although a handful of studies in non-clinical samples have explored associations between parent anxiety and child psychopathology, this knowledge is unlikely to generalize to the unique circumstances of childhood CPI. Accordingly, the aim of this dissertation was to obtain a more robust understanding of child multimorbidity, parent anxiety, and the complex association between parent anxiety and child psychopathology in this population. Opportunities to improve child and parent mental health via tailored, integrated, and family-centered approaches are emphasised.
This dissertation is comprised of three distinct longitudinal studies which examined the onset of child multimorbidity, trajectories of parent anxiety, and the association between parent anxiety and psychopathology among children with CPI. Specific objectives were to: 1) evaluate child multimorbidity onset; 2) identify multimorbidity risk factors; 3) delineate trajectories of parent anxiety; 4) identify predictors of parent anxiety trajectories; 5) quantify the association between parent anxiety and child psychopathology; and 6) assess parenting stress and family functioning as moderators of this association. Data for this dissertation come from the Multimorbidity in Children and Youth across the Life-course (MY LIFE) study, a prospective cohort study of 263 children with CPI and their parents, who were followed for 48 months.
The first study utilized survival analysis to determine that over the 48-month follow-up, 64% of children experienced multimorbidity (i.e., internalizing or externalizing psychopathology). Internalizing psychopathology was associated with greater child disability, older child age, and younger parent age, while the emergence of externalizing psychopathology was associated with male child sex and greater parent psychopathology symptoms. These findings suggest that a large proportion of children with CPI will develop multimorbidity and illustrates that multimorbidity onset is nuanced. Specifically, internalizing and externalizing psychopathologies were associated with distinct profiles of children. Findings highlight the importance of integrated physical and mental healthcare services to support the mental health of children with CPI.
The second study examined the 48-month trajectories and predictors of parent anxiety symptoms using latent class growth modelling. Four trajectories of parent anxiety were identified: minimal, mild, moderate, and high. Approximately 40% of parents had persistent, moderate or high anxiety. Risk factors for less favourable anxiety trajectories were greater depression symptoms, higher educational attainment, having a female child, and having a child with multimorbidity. Results suggested that parents of children with CPI are at-risk of persistent anxiety, and parent- and child-related variables were associated with poorer anxiety trajectories. Because parents are responsible for the care of their children, promoting parental mental health is essential to ensure their well-being and that of their children. These findings garner support for greater refinement of pediatric healthcare services to be more family-centered and include strategies to promote the mental health of parents caring for children with CPI.
The third study used linear mixed-effects modelling to examine associations between parent anxiety and child psychopathology. Results indicated that greater parenting anxiety was associated with greater child internalizing and externalizing symptoms over 48-months. Product-term interactions suggested that parenting stress and family functioning moderated the association between parent anxiety and child internalizing symptoms, while only family functioning moderated the association with externalizing symptoms. Results demonstrate the link between parent anxiety and child multimorbidity, and underscore the importance of evaluating parent anxiety, stress, and family functioning to support child mental health. These findings illustrate that to effectively manage the health of children with CPI, implementing evidence-based healthcare services which are family-centered must be prioritized.
Knowledge from this dissertation addresses critical gaps in the understanding of child CPI, including multimorbidity onset, parent anxiety, and the association between parent anxiety and child psychopathology over time. Findings from this research reinforce that children with CPI and their parents face ongoing risk of psychopathology. Moreover, parent anxiety is associated with child multimorbidity, and family environmental factors likely influence this association. This research highlights five salient opportunities to refine pediatric healthcare: 1) early and routine mental health surveillance among children with CPI; 2) integrated physical and mental health services; 3) prioritizing family-centered care strategies; 4) targeted screening and interventions for at-risk subpopulations of children and parents; and 5) evidence-based interventions tailored to the unique experiences of children with CPI. Additional longitudinal studies are needed to explicate causal mechanisms which underpin the association between parent anxiety and child psychopathology, particularly among these vulnerable families. Providing comprehensive and integrated healthcare will help to reduce the incidence of multimorbidity and ensure the best possible outcomes for children with CPI and their parents
Contextual AI: Integrating Macro-Context with Transformer Architectures for Social Media Analysis, Federated Learning, and Recommender Systems
No full textContext is crucial for understanding the world and making informed decisions. While existing transformer architectures excel at contextualizing information locally, such as other words in a sentence, they fail to factor in broader, macro-level contexts. We identify available yet underutilized macro contexts in three use cases: online discussions, federated learning, and recommender systems. For each, we motivate the need to leverage existing macro context and propose context-aware solutions based on the transformer architecture.
In online discussion boards, the rich conversational and multimodal macro context in which a comment is made is often overlooked. This is especially pertinent in hate speech detection. Classical solutions that examine individual comments in isolation fail to account for this context, leading to ambiguity and misinterpretation. For instance, the comment ``Ew, that’s gross!'' has a different interpretation depending on whether it’s in response to food or sensitive issues like LGBTQ rights. Furthermore, images that accompany text can also provide crucial context. We propose mDT, a novel deep learning model architecture based on graph transformer networks, which incorporates this valuable context when evaluating the hatefulness of individual comments. Our experimental results demonstrate a 7\% F1 improvement over existing baselines that do not utilize this context, and a 21\% F1 improvement over previous graph-based methods.
Second, we tackle the context-agnostic paradigm of federated learning. The prevalent Federated Averaging (FedAvg) method statically averages model weights, failing to account for the crucial macro-level context of heterogeneous-agent environments, leading to a suboptimal, one-size-fits-all model. For example, autonomous driving agents exploring rural roads acquire different knowledge than those in urban settings, and this environmental context is lost in the process. We propose FedFormer, a novel federation strategy that leverages transformer attention to enable each agent to weigh and selectively incorporate insights from its peers in a context-dependent manner. In turn, FedFormer enables a more effective, efficient federation that respects and adapts to environmental diversity while preserving privacy. Our experiments across environments in MetaWorld, a set of heterogeneous robotic manipulation tasks, demonstrate improvements of 1.48x to 3.41x over FedAvg.
Finally, in recommender systems, the user’s intent can provide critical personalization context. Simple approaches rely on collaborative filtering, which only models implicit (micro-level) user preferences by extrapolating from historical data. Our solution, Flare, proposes a contextual recommender system that empowers users to steer recommendations via explicit natural language queries (e.g., ``Staplers'', ``Webcams''). Flare’s architecture fuses collaborative filtering signals with semantic representations of both the user’s explicit query and item descriptions, bridging the gap between long-term preferences and the context of the user's immediate goals. Our experiments using the Amazon Product Reviews datasets show a 1.7x and 2.53x increase in recall@1 and recall@10, respectively, compared to approaches that do not factor in user intent
A Proteomic Analysis of Biological Sex and Health in Gurat, France
No full textPaleoproteomics, the study of ancient proteins, uses mass spectrometry to identify and characterize proteins by their amino acid sequences. This thesis explores the potential of paleoproteomics to inform bioarchaeological interpretations of biological sex and health in a small medieval population from Gurat, France. Ten milligram enamel samples from six individuals excavated from a rock-cut cave church were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify proteins in enamel and evaluate their interpretive potential. The primary aim of this project was to test a modified method to investigate its ability to successfully identify amelogenin (amelogenin-X and amelogenin-Y) and immune proteins (C-reactive protein and immunoglobulin-gamma). This method was intentionally modified to reduce analytical costs and resource requirements, while remaining applicable to very small quantities of dental enamel, thereby increasing its feasibility for archaeological and ethically sensitive sampling contexts.
The enamel-specific protein amelogenin was successfully identified in all six samples, allowing for proteomic estimations of biological sex to be made. In contrast, one non-enamel-specific immune protein (C-reactive protein) was identified in only one sample, reflecting uncertainty regarding the abundance and preservation of immune proteins in enamel, the immune histories of the Gurat individuals, and the sensitivity of the modified method for immune protein detection.
These results highlight both the strengths and limitations of paleoproteomics, offering avenues of exploration in future directions. Above all, this thesis finds that proteomic analyses can complement osteological analyses to offer valuable insight into archaeological populations
Cellulose Nanocrystal Coated Paraffin Wax Coating for Fog and Dew Water Harvesting
No full textFresh water scarcity is an urgent global issue. A sustainable and renewable method is harvesting atmospheric water, among which fog and dew water can be passively collected onto a surface. The efficiency of such collecting systems depends critically on the wetting and dynamic behavior of water droplets on the surface. Common approaches to modify surface topography and hydrophobicity often relies on lithographic, plasma, or fluoropolymer-based methods that are costly, complex, and environmentally unsustainable. In contrast, this work proposes a novel, simple, and bottom-up approach for producing surface with functional coatings through cellulose nanocrystal (CNC)–stabilized Pickering emulsions.
The first part of the study focuses on understanding the stabilization and formulation behavior of CNC-based oil-in-water emulsions under varying CNC concentration, ionic strength, and oil-to-water ratios. The resulting interfacial coverage and droplet packing efficiency govern the size and assembly of the wax microparticles, allowing fine control of surface roughness and wettability. Coatings derived from these particles exhibit a wide range of wetting states—from hydrophilic to superhydrophobic—depending on CNC surface coverage and aggregation state.
In the second part, these coatings are evaluated for fog and dew water collection, emphasizing the differences between liquid water deposition and humid air condensation on surface. The results show that overall water collection performance is governed by two coupled processes: the rate at which moisture is captured on the surface and the efficiency with which the accumulated water is removed. Previous studies have shown that while superhydrophobic surfaces exhibit superior droplet removal efficiency, their performance can degrade under continuous usage due to partial loss of superhydrophobicity and water film formation. On the other hand, surfaces with balanced nucleation density and drainage efficiency are more desirable, especially for condensation.
This research establishes a biobased, PFAS-free, and scalable fabrication route for tailoring surface wettability using CNC-stabilized emulsions. Beyond atmospheric water harvesting, the insights gained here into interfacial assembly and condensation dynamics under realistic humid-air conditions contribute broadly to the design of sustainable coatings for humidity control and anti-fogging/anti-icing applications
Use of atmospheric pressure spatial chemical vapor deposition to create spatially variant metal oxide semiconductor films for use in gas sensing arrays
No full textManufacturing gas sensor arrays is a key roadblock in commercially viable electronic nose systems as sensor arrays require large numbers of unique sensors. Atmospheric-pressure spatial chemical vapor deposition (APSCVD) is a fabrication method that can be utilized to lower manufacturing costs. In this thesis, APSCVD is used to create gradients of sensing materials which are then used to create an array of sensors with unique physical properties. Materials explored using APSCVD are SnO2 thickness gradients, SnO2 and Cu2O heterojunction gradients, and zinc-tin-oxide composition gradients. These materials are created using a combination of a stainless steel atmospheric-pressure spatial atomic layer deposition reactor head and a custom APSCVD reactor head designed to create metal-oxide-semiconductor thin films with physical property gradients. The custom APSCVD reactor head implements a substrate-reactor spacing gradient to achieve physical property gradients, building upon a previous work showcasing that tilting a stainless steel reactor head leads to a thickness gradient [1], [2]. The heterojunction gradient consists of a uniform Cu₂O layer with a thickness of ~103 nm and a SnO₂ layer with a thickness gradient from ~22 nm to ~12 nm, measured using ellipsometry. The ellipsometry thickness measurements show an R² value of 0.95. The energy-dispersive x-ray spectroscopy measurements of the composition gradient film show the tin to zinc ratio ranging from 0.86 to 0.21 with a R² value of 0.96. The fabricated gradient films are converted to sensors using photolithography. Interdigitated electrodes are fabricated on the top surface, and chips with 8 sensors are placed on chip carriers. A custom gas sensor testing system is created to continuously run experiments and generate response data. The test system consists of control software for heating, an Arduino-based relay for recording up to 8 sensors at a time, and mass flow controllers which auto adjust to cycle through different experiments and analytes. Ethanol, isopropyl alcohol, acetone, and water are used as analytes in this thesis. The data recorded showcases that APSCVD can be used to create functional gas sensors with thickness, heterojunction, and composition gradients. The composition gradient exhibits a response-direction inversion, resulting in an increase in resistance at room temperature and a decrease at 200 °C. Additionally, heterojunction gradient showcases a parabolically varying response across the film. Principal component analysis of heterojunction gradient sensor data shows that combining multiple sensors improves selectivity relative to individual sensors, as reflected by an increase in silhouette score from -0.02 to 0.38, corresponding to a transition from overlapping to distinct response clustering
A proof-technique-independent framework for detector imperfections in QKD
No full textThe security of Quantum Key Distribution (QKD) protocols is theoretically established using idealised device models. However, the physical implementations upon which practical
security relies inevitably deviate from these ideals. This thesis develops a rigorous and versatile framework to address a subclass of such deviations: detector imperfections.
This framework, termed ’noise channels’, is independent of security proof technique. This approach recasts imperfections as a quantum channel preceding an idealised measurement process. By granting the eavesdropper control over this channel, the security analysis is simplified to an ideal scenario, with the effects of the imperfections mathematically contained within a well-defined parameter. The utility and versatility of the framework are demonstrated through applying it to the postselection technique, and for
phase error estimation. The application to phase error estimation is an improvement over past analyses which either assumed qubit detection setups, IID attacks, or required hardware modifications. We observe a remarkably high tolerance to imperfections when using the postselection technique.
Finally, we extend the framework to address cross-round correlations, providing a methodology to prove security against detector memory effects such as afterpulsing and
dead times. This work thus establishes a structured and powerful toolkit for analysing detector imperfections in practical QKD systems, unifying their treatment across different security proof techniques and advancing the development of robust implementation security
UEPVGA: A Novel Unreal Engine 5 Based Methodology for Airport Photovoltaic Glare Assessment
No full textAirports have significant potential for deploying solar photovoltaic (PV) systems because they have large amounts of available land and high energy demands. However, the deployment of PV systems in and around airports in Canada and the United States is constrained by concerns from pilots and ground personnel regarding glare risks and formalized in policy that restricts their deployment without a comprehensive glare risk assessment. To address these issues, we developed a novel Unreal Engine PV Glare Assessment (UEPVGA) framework. The framework uses real-time game engine rendering to create photorealistic, dynamic glare simulations. It employs physically based rendering techniques to model the optical properties of PV modules that accurately reflect the relationship between incident angle and reflectance. Astronomical algorithms precisely simulate the sun's position and trajectory across the sky throughout the year. Simulated glare from the UEPVGA was validated against observational data at different altitudes and angles from real-world PV panels that were acquired by a remotely piloted aircraft. Validation results demonstrated that the simulated solar position and glare intensity of solar panels highly correlate with observational data. The framework was then used to conduct a glare assessment of a study area considering three hypothetical zones for PV panel installations. Results revealed pronounced seasonal risk patterns and identified specific high-risk zones, demonstrating the framework's practical value for operational safety planning. This study suggests the feasibility of using game engines as environmental simulation platforms and highlights their potential to support aviation safety and other fields
Media Optimization of CHO Cell Culture using a Hybrid Dynamic Flux Balance Analysis Model
No full textThe manufacturing of pharmaceuticals relies heavily on upstream cell-culture processes that must achieve high, reproducible productivity and quality under tight timelines. Mathematical modeling is an essential tool for bioprocess prediction and optimization. Classical
kinetic models provide mechanistic detail but require high parameterization, thus making them prone to overfitting and prediction inaccuracy outside the domain of conditions studied for model calibration. On the other hand, purely data-driven (black-box) models are
easy to train but extrapolate poorly due to their lack of physical constraints. This thesis addresses that gap by developing, validating, and experimentally applying a hybrid modeling framework for Chinese hamster ovary (CHO) cell culture that couples dynamic flux
balance analysis (dFBA) with partial least squares (PLS) regression models to describe
concentration-dependent kinetic constraints.
A key challenge in formulating dynamic metabolic models without overfitting is defining a minimal set of parameter-dependent constraints that is sufficient for accurate data fitting. In practice, the dominant drivers of growth and productivity include both abundant extracellular metabolites (e.g., glucose, glutamine) that can be tracked over time and minor components (vitamins, growth factors) that are often unknown due to confidentiality or only known at inoculation and are not routinely measured during the run. Direct optimization over all media components is therefore impractical. This work addresses that limitation by optimizing proportions of commercially available basal media and feeds rather than individual trace constituents. Kinetic bounds embedded in the hybrid model are then expressed as explicit functions of the media proportions, allowing the indirect,
but operationally meaningful, optimization of media without time-resolved measurements of each species.
Like many empirical and hybrid models, accuracy is strongest near the calibration domain, which can bias an optimizer if the predicted optimum lies outside the data support. To mitigate this, the thesis implements a run-to-run (batch-to-batch) optimization strategy
in which each iteration consists of (i) model identification using newly collected data and (ii) model-based optimization to recommend the next experiment. The recommendation is executed, new trajectories are acquired, and parameters are updated for the subsequent iteration, thereby guiding the model and the process toward the true optimum through successive refinements. In this study, the hybrid dFBA–PLS model is integrated with
experiments on an Ambr15® microbioreactor platform and enables efficient exploration of the media-blend simplex under consistent operating conditions. The availability of multiple parallel runs allows a design-of-experiments strategy to be layered onto the run-to-run loop, accelerating convergence to high-performing blends while quantifying variability across batches. In particular, the experimental study demonstrates the key importance of matching gradients between experiments and model predictions, an intermediate step in our methodology, to drive the process close to an optimum. Without such matching of gradients, it is shown that the optimization is not meaningful.
The overall optimization goal is to improve culture performance by identifying media blend compositions, encoded here by inoculum and feed fractions of commercial media, that maximize monoclonal antibody (mAb) production while maintaining target viability. Systematically varying these fractions tunes both major nutrients and traces in a controlled, scalable manner.
Within the hybrid model, a piecewise PLS layer maps measured states (e.g., extracellular concentrations, viable cell density) and media proportions to metabolite uptake/production rates; these rates are transformed into upper–lower kinetic bounds for selected exchange
and lumped reactions, which the dFBA layer enforces alongside intracellular stoichiometry and mass balances. In this way, the model links media composition to feasible flux distributions and, in turn, to dynamic trajectories of biomass and mAb. A key novel contribution of the modeling approach is the use of uncertainty bounds for the regression models describing the constraints. It is shown that relaxing or tightening these bounds
for the regression models provides several advantages: i- it addresses the multiplicity of solutions of dFBA by limiting the solution space, ii- it reduces overfitting by widening some bounds, thus making them less sensitive to the corresponding constraint, and iii- the relaxation of bounds for a particular constraint reduces sensitivity with respect to this constraint without the need of completely eliminating the constraint that would require expensive mixed integer optimizations.
The specific contributions of the thesis are
1. Development of a novel hybrid CHO model that combines a dFBA core with PLS defined, concentration- and media-proportion-dependent kinetic bounds, using a minimal set of tunable uncertainty parameters to avoid overfitting.
2. Implementation and validation of the hybrid model for CHO cultures conducted on Ambr15® cultures under diverse inoculum and feeding formulations, demonstrating the ability to reproduce key metabolic behaviors (e.g., lactate and ammonia dynamics) and product formation profiles. To our knowledge, this is the first CHO model of the dFBA type that explicitly accounts for mixtures of media.
3. Integration of the hybrid model into a run-to-run optimization procedure that recommends next-batch media blends to maximize mAb titer at target viability, using parallel experiments to update parameters, assess variability, and improve recommendations iteratively. This is the first application of the Ambr15® in the context of a run-to-run model-based optimization approach. The application of this methodology led, after 3 iterations, to an almost 30 percent improvement in the value of an objective function consisting of the specific productivity at 80 percent viability.
Together, these elements yield practical modeling and model-based optimization frameworks that respect physicochemical constraints, leverage data efficiently, and directly support media-blend design. By expressing kinetic limits as functions of media proportions,
the approach enables optimization over both major and minor components without requiring time-resolved measurements of every trace species. Embedding the model in a run-to-run loop further aligns the model to the plant response as the search advances toward the true optimum. The resulting dFBA–PLS methodology provides accurate, interpretable predictions and actionable guidance for upstream process development in CHO
cell culture
Systems and Control Protocols for Neutral-Atom-Array Quantum Processors
No full textNeutral atom arrays are a leading platform for programmable quantum processors, offering individual qubit addressability, long-lived hyperfine ground states, and strong Rydberg interactions. Recent progress has demonstrated coherent control over thousands of atoms. However, achieving scalable control requires precise mitigation of environmental and hardware imperfections that degrade gate performance.
This thesis presents an integrated neutral-atom array platform built from the ground up that incorporates quantum sensing directly into the processor. Each atom functions both as a qubit and a local magnetometer. We design, build, and characterize from first principles three subsystems: 1) a microwave control system for driving hyperfine transitions in ground-state rubidium atoms; 2) a Raman laser system for site-selective single-qubit gates; and 3) a Rydberg laser system with quantum optimal control for robust two-qubit gates. This work provides a universal gate set and quantifies which error sources limit performance.
First, we develop an in-situ magnetic field imaging technique using the atom array as a quantum sensor. Through site-resolved Ramsey spectroscopy, we image magnetic fields across a 260 μm × 160 μm region with 3 μm spatial resolution. We then apply computed corrections that compensate for the bias magnetic fields, producing uniform global microwave single-qubit rotations.
Second, we introduce a hardware-aware simulation framework to evaluate Raman laser systems for hyperfine qubit manipulation. Simulations predict a single-qubit gate infidelity of 4.4 × 10⁻⁴ using BB1 composite pulses to mitigate thermal motion errors. We validate the Raman laser system by building and characterizing its phase noise.
Third, we develop a Rydberg laser system for high-fidelity entangling gates. We apply linear response theory to map laser phase noise to single-atom Rydberg excitation fidelity. We then demonstrate fast phase-noise engineering by optimizing laser servo parameters. We employ hardware-aware quantum optimal control to design both Rydberg excitation and two-qubit gate pulses with built-in robustness against physical and control parameter fluctuations, outperforming analytical benchmarks.
This integrated platform demonstrates high-fidelity universal control of neutral-atom registers with hundreds of qubits. By systematically addressing environmental inhomogeneities through integrated sensing and hardware-aware control design, this work provides a validated path for scaling quantum processors while maintaining gate fidelity
Addressing Informal Caregiver Burden: Technology and Toolkit for Medication Management in Older Adults
No full textBackground: Older adults with multiple chronic conditions often manage complex medication regimens. Age-related physical and cognitive impairments further complicate this process and increase the risk of medication errors and non-adherence. Informal caregivers, including family, friends, and neighbors, play a crucial role in supporting this process. However, many caregivers feel unprepared for the complex and time-consuming tasks involved in medication management. As a result, they may experience significant caregiver burden, which affects their emotional, social, financial, and physical well-being and can contribute to anxiety, poor self-care, sleep disruption, social isolation, or even suicidal thoughts.
Objectives: 1. To evaluate the impact of an automated medication dispenser (AMD) on compassion fatigue, satisfaction, and medication administration hassles. 2. To develop a medication management toolkit to support family caregivers with medication management at home.
Methods: Study one is a pilot mixed-methods study that recruited 7 pairs of family caregivers and their older care recipients. Caregivers completed the Family Caregiver Medication Administration Hassles Scale (FCMAHS) and the Professional Quality of Life Scale (ProQoL) at baseline, 2 weeks, and 3 months after implementing AMD in care recipients’ homes. Caregivers were interviewed before and after using AMD. The interviews were recorded, transcribed, and thematically analyzed. Study two is a qualitative study in which 16 family caregivers participated in focus group discussions to identify medication management challenges and solutions that will inform the development of the toolkit.
Results: In study one, Friedman tests showed no significant change in FCMAHS subscale scores over time after Bonferroni correction (α =0.0125; all p > 0.0125). The total score (primary outcome) was assessed without correction (α = 0.05) and was not significant. Wilcoxon Signed-Rank Tests showed a similar pattern, except for a significant reduction in total score from baseline to 3 months (p=0.02). Both tests showed no significant change in scores for the subscales of ProQoL after Bonferroni correction (α= 0.0167); all p > 0.0167). Three themes emerged from the pre-intervention interviews: becoming a caregiver, approaches to support medication management, and caregiver experience and well-being. Four themes emerged from the post-intervention interview analysis: usability and functionality, experience with remotely delivered pharmacy services, caregiver experience and well-being, and impact on the caregiver–recipient relationship. Six themes emerged from study two: caregiver–recipient relationship and caregiving context; challenges with medication management; medication management strategies; non-medication management tasks; caregivers’ preferences for toolkit format and content; and additional support with medication management.
Conclusion: Study one shows that the long-term use of AMD has the potential to be beneficial for caregiving burden related to medication management but is influenced by the caregiver’s adjustment period. Future research should verify these pilot findings. Study two shows that family caregivers manage medications across several domains in which they face challenges, including scheduling doses, supporting adherence, tracking medication supplies, and communicating with healthcare professionals. To assist with these tasks, caregivers often rely on simple and traditional tools and strategies