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Targeted and non-targeted analysis of microplastics exposure using pyrolysis gas chromatography ion mobility mass spectrometry
No full textPlastics are synthetic materials composed of organic polymers and additives such as plasticizers, flame retardants, and antioxidants, some of which can adversely affect human health. Over time, larger plastics and fibers fragment through weathering, mechanical degradation, and sunlight into smaller particles known as microplastics (<5 mm) and nanoplastics (<0.1–1 μm), collectively termed MNPs. Their widespread presence raises concern about potential health risks, yet detecting trace levels remains challenging. This thesis presents two projects that provide new insight into microplastics exposure.
The first project developed a sensitive method for quantifying trace levels of polystyrene (PS), polyethylene (PE), and polyvinyl chloride (PVC) in drinking water using filtration, accelerated solvent extraction, and pyrolysis gas chromatography–ion mobility spectrometry (pyr-GC-IMS). Detection limits were 0.52 ng/L for PS, 7.1 ng/L for PE, and 13.0 ng/L for PVC, with recoveries above 60%. Application to household water samples in St. John’s (n = 6) revealed 18 ng/L of PE and 31 ng/L of PVC, corresponding to a daily intake of ~392 ng—well below estimated inhalation exposure levels.
The second project applied a non-targeted GC-cIMS method to characterize halogenated compounds associated with airborne MNPs. Several fluorinated species were tentatively identified, suggesting a potential link between indoor PFAS and textile sources contributing to human exposure
Major depressive disorder relapse in primary care
No full textBackground:
Major depressive disorder (MDD) is a chronic, recurrent illness and a leading cause of disability
worldwide. Despite the effectiveness of acute-phase treatments, relapse remains both highly
prevalent and insufficiently addressed in primary care, contributing to fragmented care, reduced
quality of life, and an increasing economic burden. This thesis examined relapse in MDD through
three complementary studies, integrating a systematic review, population-based
pharmacoepidemiology, and health economic modeling within the Canadian primary care context.
Methods:
Study 1 undertook a systematic review and meta-analysis of 35 studies to estimate relapse
incidence and compare the effectiveness of pharmacologic, psychological, and combined
interventions in primary care. Study 2 utilized linked administrative health data from
Newfoundland and Labrador (2010-2020) to examine antidepressant reinitiation after ?6-month
gaps, used as a proxy indicator for relapse among adults discharged from hospital with MDD.
Time-varying Cox regression models were employed to estimate hazard ratios (HRs) by treatment
type, age, sex, and socioeconomic status. Study 3 developed a Markov model to compare the
lifetime cost-utility of cognitive behavioral therapy (CBT) versus mindfulness-based cognitive
therapy (MBCT) for relapse prevention, from both public payer and societal perspectives, in
alignment with Canadian Drug Agency (CDA) guidelines.
Results:
Across all studies, relapse or antidepressant reinitiation occurred in approximately 30-60% of
patients within one year, underscoring the recurrent nature of MDD. In Study 2, 61% of patients
reinitiated antidepressants following hospitalization, with lower relapse risk among individuals
receiving combination therapy compared to monotherapy. Age and income emerged as significant
modifiers of relapse trajectories. Study 3 identified CBT as the more cost-effective option under
Canadian public payer assumptions (incremental cost-effectiveness ratio below commonly
accepted willingness-to-pay thresholds), while MBCT remained a viable alternative, particularly
in group-delivery contexts.
Conclusions:
Relapse in MDD is common, insufficiently managed, and influenced by pharmacologic,
psychosocial, biological and sociodemographic factors. Psychological interventions such as CBT
and MBCT are both clinically effective and economically defensible options for sustained
remission. This thesis advocates a health-system-level reframing of MDD care—embedding
relapse prevention within longitudinal, stepped, and publicly funded primary care pathways
Mors voluntaria: exploring the relationship between medical assistance in dying in Canada and suicide
No full textSince 2016, Medical Assistance in Dying (MAiD) legislation has provided immunity from prosecution for medical professionals who assist patients in dying. The MAiD law is an exemption to a section of the Criminal Code of Canada that prohibits assisting in suicide. The legislation claims that there is a difference between medically assisted dying and suicide. However, this distinction is not self-evident as medical assistance in dying is a way for a person to kill oneself. Moreover, the two acts share many important similarities: both are self-initiated; both result in the death of the individual who self-initiates the act; both can be done with or without assistance; and both can have a wide range of underlying reasonings, intentions, and wishes motivating the decisions. Given the deadly and perverse moral implications of overlap between suicide and a MAiD provision, this thesis does not uncritically accept the distinction, but instead aims to thoroughly investigate it. The findings of the analysis show that although MAiD provisions are not necessarily suicides, MAiD is not always distinct from suicide. To support these findings, presented throughout the analysis are documented cases of MAiD in which the criteria that describe a suicide clearly apply and where prevention, not assistance in dying, ought to have been the governing societal value
Design, modelling and performance analysis of a residential grid-tied and off-grid solar system
No full textThis thesis presents a comprehensive study on the design, simulation, and performance evaluation of residential solar photovoltaic (PV) systems, focusing on both grid-tied and off-grid configurations in the context of Lahore, Pakistan. With the growing energy crisis and increasing electricity tariffs in the region, solar energy offers a promising alternative for reliable and sustainable power generation. The research employs a hybrid methodology integrating techno-economic optimization using HOMER Pro and dynamic performance simulation via MATLAB/Simulink. An 11 kW grid-connected PV system was first analyzed for a residential property in Model Town, Lahore. The system utilized high-efficiency Jinko Solar modules, a DC-DC boost converter, and a Growatt inverter, operating under local climatic and tariff conditions. The analysis revealed a significantly lower Net Present Cost (USD 18,995) and a substantial reduction in grid dependency and carbon emissions, compared to conventional grid-only electricity consumption. In parallel, a detailed dynamic simulation of the same system was conducted in MATLAB/Simulink to study the real-time behavior of the PV setup under varying environmental conditions. The results demonstrated consistent power delivery, effective voltage regulation, and reliable Maximum Power Point Tracking (MPPT) using the Incremental Conductance method, even in the absence of battery storage. Furthermore, the thesis explored the feasibility of a fully autonomous off-grid PV system for the same residential setting. Utilizing HOMER Pro, the off-grid system design included a 12.6 kW solar array, 89 kWh battery storage, and an 11 kW inverter. The simulation results showed excellent economic and technical performance, including a 91% reduction in Net Present Cost compared to diesel-based alternatives, an internal rate of return (IRR) of 228%, and a short payback period of 0.49 years. Additionally, the system produced zero CO2 emissions, reinforcing its environmental benefits. The comparative assessment of grid-tied and off-grid systems provides critical insights into their suitability under different economic, technical, and policy scenarios. The study concludes that both configurations offer viable solutions for residential energy needs, with grid-tied systems being cost-effective under net metering policies, while off-grid systems ensure energy resilience in areas with unreliable grid supply. The findings of this research support the wider deployment of solar PV in urban Pakistan and provide a scalable framework for future implementation and policy development
Exploring patients' experiences accessing fertility services and information: a journey of advocacy and empowerment
No full textIntroduction. Infertility is broadly defined as an inability to conceive and affects an estimated one in six people worldwide. Infertility has primarily been researched and conceptualized as a medical condition, and limited research has examined the experience of waiting for fertility services. Purpose. This study aimed to explore female and non-binary patients' experiences waiting for fertility care and services in the Canadian province of Newfoundland and Labrador. Methods. Five female patients currently awaiting fertility services in Newfoundland and Labrador were interviewed, and data were analyzed using qualitative, interpretive phenomenological analysis methodology. Results. Four themes emerged: (1) the emotional urgency of waiting, (2) seeking clarity through information, (3) shortcomings of the system, and (4) an ideal world. Participants highlighted a sense of emotional burden and urgency, their health information-seeking needs, and characteristics of the healthcare system that contributed to unsatisfactory experiences during the waiting period. Participants also highlighted two patient-centred and practical strategies to improve fertility patients' waiting experiences: (1) improved communication from the provincial health authority and (2) maximizing the waiting period. Conclusion. Interviews with participants highlighted several experiences associated with waiting for fertility care as well as strategies to improve waiting experiences
Exploring perceptions and readiness for nursing contributions to genomics-informed cancer care in Newfoundland and Labrador: a covergent mixed methods study
No full textBackground: Newfoundland and Labrador (NL) has a high burden of genetic cancer predisposition syndromes (CPS), with many affected or at-risk persons reporting related unmet healthcare needs. Many genomic applications demonstrated to improve cancer outcomes are now indicated in routine cancer care. While nurses have central roles in cancer care, overall, the adoption of genomics-informed practices has lagged across the global nursing profession.
Purpose: In anticipation of an upcoming provincial clinical translational genomic service, this study explored the perceptions and readiness of oncology nurses and individuals with a CPS in NL, to inform potential directions for nursing contributions to genomics-informed clinical care.
Methods: A convergent design and a patient-oriented research approach were used in this mixed method study. In the quantitative stream, 50 NL oncology nurses completed a cross-sectional survey with validated measures of nurses' genomic knowledge (the GNCI©), and their attitudes, practices, and influence of the social system related to genomics-informed practices (questions from the GGNPS). In an interpretive description (the qualitative stream), 37 persons with a CPS in NL were interviewed about their experiences and perceptions of genomics-informed health and nursing care. Each study stream was analyzed and reported separately. Subsequently, integration analysis was guided by an interpretive descriptive theoretical scaffold. The Pillar Integration Process (PIP) was the technique used to develop mixed methods inferences.
Results: Three overarching inferences were generated: (1) Genomic testing and related patient inquiries are occurring in cancer care, yet nurses and patients face uncertainty about where to turn next for answers. (2) While not the current status quo, nurses and patients share a recognition of the value of applying a genomics lens to existing nursing roles, including family history collection, navigation, and supportive care. (3) Both groups endorsed benefits of a prospective longitudinal high-risk hereditary cancer follow-up service, with patients reporting that specialist oncology nurses could contribute to this service.
Conclusion: These findings provide patient and provider perspectives on potential avenues for nursing roles in person-centred genomics care that are responsive to the needs of this patient population. These avenues can be facilitated through workforce development, policy initiatives, and future patient-oriented, participatory research involving multiple end-users
Investigating soil dissolved organic matter composition to inform its fate in furture warmer and wetter boreal forests
No full textBoreal forests store large amounts of soil organic carbon (SOC) yet rising temperatures and intensified precipitation due to climate change are increasing the vulnerability of these stocks. The movement of dissolved organic matter (DOM) is a primary pathway of carbon transfer to and from mineral soils; however, current carbon budget models do not account for carbon storage and loss via DOM transfer, despite the evidence of increased DOM mobilization under warmer and wetter conditions. Improving the accuracy of global carbon models requires a clearer understanding of the processes associated with DOM retention and release in boreal forest mineral soils. This thesis describes experimental investigations of: (1) how two main soil processes, microbial degradation and mineral soil physicochemical processes, alter DOM composition, and (2) how initial soil moisture and soil carbon saturation impact the composition of DOM retained or released by mineral soils over event-based time scales (days-weeks). This study demonstrated that coupled monitoring of soil DOM aromaticity and amino acid composition described the degree and type of transformation DOM undergoes on an event-based timescale. Monitoring these characteristics can provide insight into the fate of DOM and mineral SOC stores with increasing climate change
Quantum machine learning for intelligent health monitoring and prediction in wearable systems
No full textThe rapid development and integration of wearable technologies or Internet-of-Things
(IoT) into healthcare technologies with computational methods holds transformative
potential for future healthcare systems, especially for personalized health monitoring
and early disease detection. In parallel, recent advancements in quantum computing
have o↵ered significant interest in quantum machine learning (QML), an intersection of
quantum computing and machine learning, which provides a novel hybrid computational
paradigm capable of addressing complex healthcare challenges that remain unsolvable
with classical machine learning techniques. This thesis explores the application of
quantum-enhanced recurrent neural network architectures, specifically Quantum Long
Short-Term Memory (QLSTM) and Quantum-based Gated Recurrent Unit (QGRU),
for predictive modeling and activity classification in wearable IoT systems, focusing
particularly on their applicability and efficacy to elderly populations. One of the
key challenges in modern healthcare intelligence systems is the simple but accurate
estimation mechanism of Physical Activity Energy Expenditure (PAEE), which is an
essential factor for assessing physical health status, particularly for healthy aging. In
order to address this challenge, the first work proposes a model called an enhanced
QLSTM with linear layer (eQLSTML) that integrates Variational Quantum Circuits
(VQCs) into classical LSTM architectures. This integration notably explores the
advantages of quantum computing, including properties such as entanglement and
superposition, to enhance the model’s ability to capture complex temporal dependencies
and subtle variations in human activity patterns. The modelling experiments were
evaluated using the publicly available GOTOV Human Physical Activity dataset,
which includes accelerometer data collected from older adults engaged in various
daily activities. In addition to modelling architecture analysis, this thesis provides
comprehensive analyses concerning the scalability, feasibility, and computational
complexity of the proposed quantum-enhanced models compared to the classical
backbone. The overall results demonstrate that the proposed quantum-enhanced
approach significantly outperforms traditional classical machine learning algorithms,
which demonstrates the potential ability for accurate predictions and robustness to
noisy data like accelerometer data. In addition, accurate classification of activities
of daily living (ADLs) is another critical aspect for early disease detection and
e↵ective healthcare intervention. Within the context of this thesis, the second work
introduces a novel hybrid model called QGRU-Multiclass Classifier (QGRU-MC).
The data preprocessing involves creating a statistical feature extraction and applying
oversampling techniques – SMOTE-ENN (Synthetic Minority Oversampling Technique
with Edited Nearest Neighbors) for the publicly available dataset called “Dataset
for ADL recognition with Wrist-worn Accelerometer”. The preliminary findings
suggest that our model has good potential for healthcare applications, particularly in
advancing future intelligent systems focused on daily activity monitoring. Specifically,
QGRU-MC achieved significant improvements in classification accuracy, sensitivity,
and specificity across multiple categories of daily activities, highlighting its potential
for practical deployment in intelligent wearable systems aimed at monitoring and
supporting healthy aging. Furthermore, this work also discusses the future research
directions and practical considerations for transforming from the simulators to modern
real quantum computers in the noisy intermediate-scale quantum (NISQ) era for a
more comprehensive view on exploiting potential impacts on public health policies and
personalized healthcare systems. Overall, simulator-based results indicate promising
potential for integrating QML methods into wearable healthcare applications such as
predictive analytics, personalized treatments, and health disease intervention to improve
healthcare outcomes. Specifically, on PAEE regression, the proposed eQLSTML
improved R² by 13.2% and reduced RMSE and MAE by 14.8% and 19.1%, respectively,
relative to the classical LSTM; on ADL, QGRU-MC demonstrates good macro-averaged
metrics across all eight classes (see Chs. 3–4). This research contributes significantly
to the growing body of literature supporting quantum computing technologies in
healthcare applications, making QML an incredible tool for addressing complex healthrelated
computational challenges in the era of big and complex data in medical fields
and smart healthcare technologies, especially in e↵ectively solving data from multiple
sources, wearable accelerometry, and multi-class ADL recognition with complicated
tasks
The development of a transition to practice framework for nurses newly hired to acute adult inpatient psychiatry
No full textBackground: There is currently an international, national, and provincial nursing shortage that is expected to persist into 2030. The province of Nova Scotia has implemented several initiatives to recruit and retain nurses; however, shortages remain in specialty areas such as inpatient psychiatry. A transition-to-practice (TTP) program tailored to meet the needs of nurses newly hired to an acute adult in-patient psychiatry (AAIP) unit in the province is needed; however, a TTP framework to guide the development of the program is a necessary first step. Purpose: The purpose of this practicum project was to develop a TTP framework for an AAIP unit in Nova Scotia to guide the development of a TTP program for nurses newly hired to this area of practice. Methods: To inform the development of this framework, a rapid literature review, consultations with key informants, and an environmental scan were conducted. Results: Results of the rapid review, consultations, and environmental scan provided valuable information regarding the structure, content, and implementation of TTPs in AAIP settings. Information regarding the evaluation of TTPs was a noted gap. Conclusion: The AAIP TTP Program Framework developed as a part of this practicum project will provide the necessary parameters within which to engage in the next steps of content development, implementation, and evaluation of a TTP program in an AAIP unit in rural Nova Scotia
Quantifying Atlantic halibut (Hippoglossus hippoglossus) migratory diversity and stock mixing across the Northwest Atlantic and implications for fisheries management
No full textUnderstanding spatial population structure and movement dynamics is essential for the sustainable management of widely distributed marine species, particularly those that support high-value fisheries. This thesis investigates the migratory diversity and stock mixing of Atlantic halibut (Hippoglossus hippoglossus) in the Northwest Atlantic. Here, I used pop-up satellite archival tags (PSATs) deployed on the Scotian Shelf and Southern Grand Banks between 2012 and 2020 to reconstruct individual movement tracks with a Hidden Markov geolocation model and identify distinct migratory strategies and putative spawning locations. I identified five distinct migratory strategies: shelf residency, slope residency, shelf-channel migration, shelf-slope migration, and dispersal. In addition, I compiled a large collaborative tagging database across Canada, France, and the USA, integrating 187 electronic and 18,795 conventional tagging records. These data were used to quantify seasonal mixing within and among Northwest Atlantic Fisheries Organization (NAFO) divisions and across national boundaries. While most fish displayed regional fidelity, movements across international and stock boundaries were observed, particularly in the Cabot Strait and on the southern Scotian Shelf.
These findings highlight how Atlantic halibut in the Northwest Atlantic exhibit a high degree of migratory diversity and connectivity with adjacent jurisdictions, underscoring the limitations of static, politically defined stock units. Effective management of this transboundary species will require not only the incorporation of fine-scale movement data into assessment frameworks, which is now facilitated by seasonal mixing rate estimates from this study, but also strong collaboration among regional and national authorities. By combining electronic and conventional tagging data from multiple countries and organizations, this thesis provides a model for cooperative research and integrated data-sharing, an essential foundation for sustainable, ecosystem-based fisheries management across borders