Master's Projects
Permanent URI for this collection
Browse
Recent Submissions
Item Empowering the Victoria community to care for animals: Addressing the gaps in services for pet guardians experiencing poverty and homelessness(2026) Hamill, Emma; Krawchenko, TamaraVictoria, BC has one of Canada’s highest per-capita homelessness populations. With visible pet ownership among this group, their experience is intensified by rising living costs, scarce affordable and pet friendly housing and limited access to animal-related supports. Partnering with the BC Society for the Prevention of Cruelty to Animals (BC SPCA) as it shifts toward a community-care model, this project examined how local services can better support vulnerable guardians, reduce stigma, and strengthen supports. Using a mixed-methods design, data was collected in the Spring of 2025 from 33 service users and 8 service providers through trauma informed, accessible questionnaires, supplemented by publicly available organizational information. Quantitative data was analyzed descriptively, while qualitative responses underwent thematic analysis, resulting in 11 themes and 10 subthemes that highlighted strong emotional bonds and reciprocal relationships, substantial structural barriers particularly veterinary costs and access and widespread appreciation for low-barrier compassionate services. Service providers reported diverse offerings but faced chronic limitations including funding shortages, veterinary capacity constraints, foster shortages, and geographic barriers. These findings led to the development of the Victoria Pet Survival Guide. This guide is purposefully written in plain language, intended to be available digitally and physically accessible as a resource consolidating information on veterinary care, pet-friendly housing, emergency boarding, food banks, and lost and found supports. The project identifies opportunities for expanded services, cross-sector collaboration, mobile and low-cost veterinary initiatives, and broader advocacy to reduce systemic barriers. Together, this work provides a practical tool and strategic direction for improving community-based animal welfare supports and helping keep people and their pets together.Item Validation of a genetic-algorithm-optimized coplanar stripline filter using HFSS and ABCD matrix modeling(2026) Asadi Shad, Tannaz; Smith, LeviThis work presents a hybrid design framework for terahertz (THz) filters that integrates genetic algorithm (GA) optimization with ABCD matrix modeling and full-wave validation using HFSS. The proposed approach enables efficient inverse design by combining fast circuit-level analysis with electromagnetic accuracy. The developed framework, based on the work of Ali Dehghanian (2025) [32], employs a GA to explore a binary design space representing metallic and dielectric pixel distributions. Each candidate geometry is evaluated using an analytical ABCD model, enabling rapid calculation of S-parameters during optimization. The final geometry is then validated through finite-element simulation in HFSS to ensure physical accuracy. Two filter types were designed and evaluated: a low-pass filter (LPF) and a band-stop filter (BSF), with performance analyzed across frequencies ranging from 0.25 THz to 2.0 THz. For the LPF, the results show that at lower frequencies (0.25 THz), the design achieved strong attenuation (–10.2 dB) but required higher structural complexity and slower convergence. As the frequency increased, the optimization became more stable and efficient, with consistent convergence behavior and improved transmission characteristics. At higher frequencies (1.5–2.0 THz), the LPF demonstrated faster convergence, reduced structural complexity (as low as 12 rows), and stable performance with fitness values around –6.3 dB. For the BSF, a similar trend was observed. Lower frequencies exhibited wider stopbands but slower convergence, while mid-range frequencies (0.75–1.0 THz) showed improved stability and faster convergence. At higher frequencies, the BSF achieved stronger notch characteristics and more efficient optimization, with the best performance observed at 2.0 THz (–4.844 dB), along with smooth convergence and reduced parameter sensitivity. Across both filter types, the results indicate that increasing frequency leads to improved optimization efficiency, reduced structural requirements, and more stable convergence behavior. A comparison between ABCD-based analytical results and HFSS simulations shows strong agreement in both magnitude and phase responses, validating the accuracy and reliability of the proposed GA–ABCD–HFSS framework. Overall, the proposed methodology provides a fast, consistent, and physically reliable approach for designing high-performance THz filters.Item A performance evaluation of collective communication libraries(2026) Srinivasan, Subiksha; Wu, Kui; Prakash Champati, JayaCollective communication operations such as AllGather and AlltoAll are fundamental to high-performance computing (HPC) and large-scale machine learning workloads. Their performance, however, is tightly constrained by network structure, link latency, and bandwidth availability across modern multi-GPU and multi-node systems. As systems scale and become increasingly heterogeneous, traditional collective scheduling approaches, which often assume unrealistic symmetry in latency and topology, become ineffective. This project investigates Traffic Engineering for Collective Communication (TE-CCL), an optimization-based framework that formulates collective scheduling as a Mixed-Integer Linear Programming (MILP) problem. TE-CCL explicitly incorporates link-level latency (α) into its scheduling formulation, enabling more realistic modelling of heterogeneous multi-fabric GPU clusters. This project examines how varying α across links affects routing decisions, epoch schedules, and solver behaviour. By introducing heterogeneous α values—rather than assuming a fixed latency across all links—the model adapts its schedules to prioritize low-latency paths, reduce hop count where beneficial, and capture realistic communication delays found in the cloud and datacenter clusters. This work provides an analysis of TE-CCL under latency variability, evaluating solver behaviour, schedule structures, and topology sensitivity across multiple cluster designs. The study highlights how α-aware scheduling reshapes the communication patterns selected by the solver and provides insights into when and why topology-regularity influences optimization stability. Overall, this investigation clarifies the importance of latency modelling in collective communication and offers guidance for extending TE-CCL toward more robust, topology-adaptive scheduling strategies for next-generation HPC and ML systems.Item Deployment of a real-time face mask classification system using browser webcam streaming and FastAPI(2026) Venkatraman, Yazhini; So, Poman; Popli, NavneetThis project presents a real-time face mask classification system designed to support safety monitoring in public and controlled environments such as workplaces, institutions, and healthcare facilities. The system detects a person’s face and classifies mask usage into four categories: with mask, without mask, with N95 mask, and improper mask. A curated dataset of face images was preprocessed through face detection, cropping, resizing, normalization, and augmentation to improve the model’s robustness under different lighting and orientation conditions. The model is built using a MobileNet based convolutional neural network, chosen for its efficiency and suitability for real-time applications. A classical Single Shot Detector is used to localize faces before classification. The trained model is evaluated using standard metrics including accuracy, precision, recall, F1-score, and a confusion matrix and achieves strong performance across all four mask categories. A live webcam interface has also been implemented to demonstrate real-time inference and practical usability. Overall, this work shows that a lightweight deep learning pipeline can reliably classify mask wearing conditions in real time on standard hardware. The system forms a basis for further improvements, such as handling complex occlusions, expanding the dataset with more diverse samples, and deploying the model as a standalone desktop or mobile application for real-world monitoring needs.Item More than decision making: How local elected officials navigate support for community-based initiatives(2026) McLean, Matthew; Siemens, LynneThis study examines how local elected officials in British Columbia support community-based initiatives (CBIs), defined as community-developed and community-run projects that provide services or benefits to residents. While CBIs are primarily driven by citizen action, elected officials are increasingly involved in supporting this work, yet their specific practices remain underexamined. Addressing this gap, the research explores the approaches elected officials use, the opportunities and challenges they encounter, and the strategies they employ to navigate tensions arising from their dual community leadership and governance roles. Using an interpretive mixed methods design, the study combined a province-wide survey of 44 local elected officials with two focus groups involving eight participants. The survey identified a broad range of support practices, while the focus groups validated findings and explored underlying tensions and decision-making strategies. Despite a modest sample size, participants reflected the demographic and geographic diversity of local elected officials in British Columbia. The study identifies 57 distinct approaches used by elected officials to support CBIs, grouped into categories that include public statements of support, resource provision, relationship building, direct services, providing guidance, championing initiatives and status work. Relationship building emerged as particularly central, reflecting the boundary-spanning nature of the elected role. Findings also highlight key outcomes elected officials seek when supporting CBIs, alongside persistent challenges including time constraints, capacity limits, competing priorities, and concerns about role boundaries. In response, the study offers six practice-oriented recommendations to support more intentional, effective, and sustainable engagement with CBIs. Overall, the research provides an early but robust framework for understanding how local elected officials support community-based initiatives and offers practical guidance to inform future practice and research in this evolving area of local governance.Item Fusion of multi-exposure videos in the gradient domain(2025) Rawat, Aryan; Agathoklis, Panajotis; Sima, MihaiGradient-domain processing has proven effective for image fusion tasks by preserving local contrast and structural details while avoiding intensity-domain artifacts. However, most existing fusion methods are designed for still images and fail to address temporal consistency when applied frame-by-frame to video sequences, often resulting in flicker and temporal instability. This seminar presents a novel approach for multi-exposure video fusion formulated directly in the three-dimensional gradient domain. The proposed method operates on spatial-temporal gradients extracted from registered exposure sequences and fuses them using a gradient-selection strategy. Reconstruction of the fused video is performed using a wavelet-based 3-D Haar decomposition combined with an iterative Poisson solver, ensuring both spatial fidelity and temporal coherence. Experimental results on standard video datasets demonstrate that the proposed method effectively enhances visual detail while significantly reducing temporal artifacts compared to classical intensity-based fusion techniques. Quantitative evaluation using spatial and temporal metrics further confirms the advantages of gradient-domain fusion for video applications.Item Stock prediction using different machine learning techniques with the dataset of NVIDIA(2025) Chen, Ying; Baniasadi, AmiraliThis project investigates the effectiveness of machine learning models in predicting NVIDIA Corporation's stock price movements. Four distinct ML approaches were implemented and compared: LR (Linear Regression), SVM (Support Vector Machines), NN (Neural Networks), and LSTM (Long Short-Term Memory networks). Using historical price data and technical indicators, model performance was evaluated through RMSE (Root Mean Squared Error) and R² (R-squared) metrics. The results demonstrated that LR and SVM models performed better than complex deep learning architectures, achieving superior accuracy, with RMSE ≈ 3, R² ≈ 0.99, while maintaining interpretability. The Neural Network model performed unexpectedly and poorly, with R² = -0.50, suggesting significant overfitting challenges. While the LSTM showed promise for capturing temporal dependencies, it required further optimization to achieve higher accuracy and compete with traditional methods. In addition, the project incorporated the importance of ethical considerations, including bias mitigation strategies and regulatory compliance measures for financial AI applications. The critical balance between model complexity and practical utility was highlighted in stock market prediction, emphasizing that sophisticated architectures don't automatically guarantee better performance.Item Operationalizing gender transformative research principles: Draft guidelines for the Citizen Science Gender-Transformative Approach to Integrating Adolescent-Friendly Family Planning and Post-Abortion Care project (CAFFP-PAC)(2026) Nassimbwa, Jacqueline; Kakuru, DorisThis project develops operational guidelines for Gender Transformative Research (GTR) to support the Citizen Science Gender-Transformative Approach to Integrating Adolescent-Friendly Family Planning and Post-Abortion Care (CAFFP-PAC) project in Northern Uganda. Adolescent Sexual and Reproductive Health (ASRH) is a significant public health challenge in the region, with rates of teenage pregnancy and unsafe abortion estimated to be 28% and 46% respectively, in just Lira District. This problem is deeply rooted in unequal gender norms and power relations. GTR offers a research framework to address these root causes by actively challenging the structures that perpetuate inequality, moving beyond mere inclusion. This project employs a systematic document analysis, evaluating the CAFFP-PAC project design against four core GTR principles: grounding knowledge in lived experiences, using a complex intersectional understanding, challenging power at multiple levels, and maintaining an intentional, action-oriented design. The analysis identifies strength in the CAFFP-PAC project’s innovative citizen science model that empowers adolescents, especially girls and other underserved groups, as co-creators of knowledge. However, it also highlights critical gaps, including an insufficient focus on transforming structural power dynamics, a lack of explicit mechanisms for full community ownership of the research process, and the absence of economic empowerment components to ensure sustainable change. The primary contribution of this work is a set of ten practical, evidence-based draft guidelines (Annex 1) structured around the CAFFP-PAC project’s phases. These guidelines propose actionable recommendations to strengthen the project's transformative potential and help bridge the gap between GTR theory and the practical implementation of the CAFFP-PAC project.Item Situation awareness in virtual and hybrid care: A systematic mapping and synthesis review(2025) Myronuk, Lonn; Roudsari, AbdulThe rapid expansion of virtual care (VC) during the COVID-19 pandemic has fundamentally transformed healthcare delivery, introducing new complexities for individuals and teams responsible for maintaining situation awareness (SA) across physical and virtual boundaries. This systematic mapping and synthesis review charts the landscape of existing literature at the intersection of SA and virtual/hybrid care (VC/HC) in clinical practice. Guided by the Population, Concept, Context (PCC) framework, systematic searches were conducted in MEDLINE, CINAHL, Embase, APA PsycINFO, Scopus, and Web of Science bibliographic databases and grey literature searches in Google Scholar and ProQuest Dissertations & Theses, seeking studies on SA in real-world clinical implementations of VC and HC. Searches were performed in September 2025. Additional references were identified by searches of reference lists. Searches identified 2,410 results. After review, 31 articles were included for analysis and synthesis. The review reveals that while SA is widely recognized as critical for safe and effective decision-making in dynamic healthcare environments, its conceptualization, measurement, and operationalization in VC/HC contexts remain poorly defined and underexplored. Most studies rely on intuitive understandings of SA, without explicit definition or theoretical grounding, and predominantly employ descriptive, atheoretical approaches. Endsley’s three-level SA framework of perception, comprehension, and projection is the dominant theoretical model cited, though distributed and system-level perspectives are increasingly acknowledged. Thematic analysis identified 28 factors influencing SA in VC and HC, clustering into eight overarching concepts: available information, technology capabilities/performance, individual cognitive/sensory factors, teamwork, workflow, user interface, education/training, and policy. Outcomes attributable to SA are reported in a minority of studies, with positive effects of VC on SA often asserted, particularly in hybrid distributed teams, without robust empirical support. The absence of controlled vocabulary terms for SA and HC in bibliographic databases further complicates systematic identification and synthesis of relevant literature. This review identifies that priorities for future research in this area should include extension of literature scoping to encompass studies of SA in clinical simulations and field exercises involving VC/HC; emphasis on using explicit definition and operationalization for SA in VC/HC research; articulation of consensus definitions and descriptions for hybrid models of care; and continued transdisciplinary practices of incorporating insights from scholarship in human factors, sociotechnical systems, and teamwork theory.Item Sign language recognition using SVM, CNN, RF, and Xception models(2025) Adil, Mohammad Abbas; Gulliver, Thomas AaronSign language is an essential means of communication for individuals with hearing and speech impairments. It enables them to express thoughts and emotions through hand gestures. With advances in computer vision and machine learning, recognizing these gestures through automated systems has become an active research area. The goal of this study is to develop an efficient system for static hand gesture recognition using supervised machine learning models and compare their performance. This study uses two distinct datasets of hand gesture images that are openly accessible on Kaggle. The first dataset, called gestures (hand), has 16,000 preprocessed grayscale images in eight different gesture classes: fist, five, okay, peace, rad, straight, thumbs, and none. The second dataset, hand gesture recognition, contains an additional 4,000 preprocessed grayscale images for the same gesture classes. These datasets collectively provide 20,000 images for this study. The first dataset is used for training and validation, and the additional dataset is used for testing. Image augmentation techniques are applied to improve the diversity of training samples and enhance generalization. Four models are implemented: Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forest (RF), and Xception. The SVM model is trained using an RBF kernel with different regularization values (C = 2, 4, 6, 8, 10, 12, 14). The CNN and Xception models are evaluated with early stopping patience values ranging from 1 to 7. All models are implemented and tested using Python in the Kaggle notebook environment. The performance of each model is evaluated using accuracy, precision, recall, F1-score, and training time. The results show that the CNN model achieves the best overall performance with an overall accuracy of 99.20% and a training time of 5.89 min for a patience value of 5. The Xception model has 99.08% overall accuracy with the same patience value, but with a higher training time of 11.02 min. The SVM classifier achieves a maximum overall accuracy of 90.83% at C = 10 with a training time of 20.35 min. The RF model achieves a maximum overall accuracy of 83.68% for n_estimators = 200 with a training time of 0.46 min. These results highlight the effectiveness of deep learning approaches, especially CNN, in real-time gesture recognition.Item Effective index analysis of bowtie aperture plasmonic waveguides via TM–TE mode decomposition(2025) Nagi, Udhav; Gordon, ReuvenA Modified Effective Index Method (MEIM) is presented for determining the propagation constant of nanoscale bowtie aperture waveguides operating across the 𝟓𝟎𝟎 − 𝟏𝟎𝟎𝟎 𝒏𝒎 spectral range. The approach uses staircase discretization to approximate the tapered bowtie geometry and employs a geometry-driven formulation to maintain numerical stability for sub-𝟑𝟎 𝒏𝒎 features. The transverse-magnetic (TM) response is computed using the hyperbolic-tangent dispersion relation for a three-layer metal–insulator–metal (MIM) stack, while the transverse-electric (TE) contribution is incorporated using a multi-slab transfer-matrix method (TMM) that enforces field continuity across discretized slabs. By combining these formulations, the model reproduces the expected plasmonic behaviour, including a monotonic decrease in effective index with increasing wavelength and stronger confinement for smaller bowtie gaps. Using wavelength-dependent Silver permittivity data, the MEIM results agree well with the rectangular-slab reference problem, confirming the correctness of both the TM and TE formulations. The framework remains computationally efficient, physically interpretable, and easily extendable to other subwavelength apertures, offering a useful preliminary design tool for plasmonic waveguides and sensing structures.Item Machine Learning-Guided Optimization of Defects in In-Situ Alloyed Additively Manufactured Parts(2025) Shelesh Nezhad, ShaafIn-situ alloying during laser powder bed fusion (LPBF) offers great compositional flexibility but is prone to process-induced defects such as lack of fusion, porosity, unmelted particles, etc. To address this problem, we developed a machine learning framework to predict and minimize major defects such as porosity (originating from lack of fusion or keyholing) and unmelted Nb particles in LPBF-fabricated in-situ alloyed Ti-45Nb. For this purpose, two least-squares boosting (LSBoost) ensemble regressors were trained using five process parameters (part shape, laser power, scan speed, hatch spacing, and scan rotation), along with their polynomial and interaction terms, to capture nonlinear relationships. These models achieved high accuracy, with R² values over 0.98 in both models. The grouped permutation importance revealed that porosity is primarily governed by hatch spacing and laser power, whereas unmelted Nb particles are primarily governed by laser power and scan speed. The models were implemented in two graphical interfaces: a forward predictor for real-time defect estimation and an inverse optimizer for identifying low-defect parameter sets. Together, they establish a unified, data-driven approach for defect-aware process optimization in in-situ alloyed systems, offering a pathway towards reproducible, low-defect additive manufacturing.Item A fire to last until morning: What's the point of art in the apocalypse?(2025-12-04) Lowey, Braedon George"A Fire To Last Until Morning" is a documentary film that features authors, scholars, journalists, and artists who all work at the intersection of climate change and humanities to address why their work is important in the ongoing efforts to preserve our natural world for future generations. By focusing on specific voices in three different locations — Lytton and Victoria in B.C., and Reykjavik in Iceland — "A Fire" highlights case studies of art and scholarship making impacts in the world from individual to generational scales. The first segment of the film interviews poet Meghan Fandrich in Lytton and concludes that relating experiences of climate change and natural disasters through artistic mediums helps creators and consumers, as well as members of impacted communities, process climate-related trauma. The second section features journalist Sean Holman and playwright Chantal Bilodeau, and concludes that exposure to eco-narratives, both real and fictional, drives people towards more sustainable decision-making and more hopeful feelings about climate change. The third section features Icelandic author Andri Snær Magnason and musician Néfur, and concludes that art can connect us to nature in innovative ways, forming or bolstering connections to the land that we see changing. The film ultimately argues that art is one of the most effective ways of sustaining attention on climate change, and is essential for both driving people towards environmentalist causes and empowering those who are already acting in the planet's best interests.Item Shared Streets: A Case Study of Bear Street in Banff, Alberta(2025-11-20) McDonald, KierstenItem Malware Detection and Categorization Using ML and LLMs(2025-12-08) Singh, DamanpreetThe rapid growth of malware attacks has created an urgent need for automated systems capable of accurately detecting and understanding malicious behavior. This project presents a comprehensive work for Malware Detection and Categorization using Machine Learning and Large Language Models (LLMs). The system's goal is to improve cybersecurity by not just detecting malware but also producing concise, intelligible descriptions of every threat it finds. The Microsoft Malware Classification dataset, which comprises approximately 21,000 malware samples grouped into nine primary families with corresponding .byte and.asm files, was adopted for the project. Since only malicious samples were present in the original dataset, roughly 15,000 benign files were added to enable binary categorization of malicious and non-malicious programs. XGBoost, LightGBM, SVM (RBF), and KNN were among the machine learning models that were trained and tested independently on both datasets. By applying the SMOTE technique, the dataset imbalance was reduced, thereby improving classification accuracy and mitigating bias toward the majority ofmalware families. Using the SMOTE technique, class imbalance was addressed. The models were evaluated for both binary classification (malicious vs. benign) and multi-class family prediction, achieving high detection performance. To enhance interpretability, an LLM-based explanation module was integrated. Following classification, the anticipated malware family is sent to an LLM (through Ollama), which produces a natural-language synopsis outlining the traits, actions, and defenses of the malware. Users can upload files, view predictions, and read the generated explanations in real time thanks to an intuitive Gradio interface. In order to provide both technical accuracy and human interpretability, the developed system successfully blends large language models for explainable analysis with machine learning for precise detection. By assisting researchers and security analysts in proactive malware defense, this method advances the field of intelligent cybersecurity by bridging the gap between detection and comprehension.Item The Substance of Harm Reduction: A Framework for Youth Supportive Recovery(Lewis Rhodes, 2026-11-26) Rhodes, LewisThis master's research project develops the Harm Reduction Recovery for Youth (HRR-Y) framework, a structured practice framework integrating harm reduction and recovery-oriented care within a residential substance use program for youth aged 15 to 21 at Threshold Housing Society’s Supportive Recovery Program (SRP). Using qualitative data from staff interviews and daily logs, the project translates frontline staff expertise into a six-tier framework spanning relational residential care, recovery programming, behavioural interventions, medication protocols, substance substitution, and supervised safer use. Each tier of interventions plays an important role in supporting residents to manage their substance use. Positioned within the context of British Columbia’s ongoing toxic drug crisis and a contentious policy landscape, the framework advances youth substance use supports by combining harm reduction and recovery principles with therapeutic residential care. This framework could be further strengthened by incorporating youth voices in future research, additional iterative opportunities for staff-led refinement, and conducting outcome evaluations.Item Enhancing Public Participation in the Town of Lunenburg, Nova Scotia(2025) Byrne, KaylaThis project examines public participation in the Town of Lunenburg, Nova Scotia, identifying challenges and opportunities to enhance engagement processes. The research addresses gaps in Lunenburg’s current engagement practices and provides practical recommendations to embed public participation as a core principle of municipal operations. Drawing on local stakeholder perspectives and best practices as identified in the literature review, this study attempts to offer a roadmap for improving engagement and public participation in ways tailored to Lunenburg’s needs and wants.Item Aiitoohtsimit ‘Blackfoot Trails’(2025) Provost, Conroy (Piinotóyi Saahkómaapi)"The purpose of this project was to explore how technology can be used and or embraced to help keep our culture and language alive in the Blackfoot context. By creating a story of language re-membering through the field of Language reclamation in the form of an autoethnographic documentary film, I wanted to share the story of my Blackfoot language journey. I returned home to Canada in the winter of 2025 to film for 8 eight weeks. Using a narrative method I reflect on who I am, where I come from and what it means to truly connect with my Language and Culture by learning from an Elder William Big Bull of The Piikani Nation for myself and for the future generations. This research project was done in a Blackfoot framework and done with an approach that is rooted in Blackfoot values and conscience. Through my journey to explore the stories and histories of our Language (Niitsipowahsinn) and Culture I begin to develop relationships with the plants, the land, the ceremonies, and the people. I gain a thoughtful understanding of the importance of pronunciation and the knowledge that is encoded with proper articulation of Niitsipowahsinn (Blackfoot Language). I relate my emerging understanding that Language acquisition necessitates confronting my own colonial shame and insecurities. Finally I reflect on the healing of the journey, and on the experiences and feelings of reconnecting with Niitsipowahsinn (Blackfoot Language)."Item Gigi’ihl Algyax ehl Angooga’m: Seeking spoken language, from our Ancestors(2025) Starlund, Jessica (Sinensxw)This research explores the application of Total Physical Response (TPR) as a method for teaching Sim Algyax (Gitxsan language) within a school context that emphasizes both language revitalization and land-based learning. Using autoethnography as the primary methodology, I documented my personal journey as a Gitxsan language learner and educator through reflective journaling. These journal entries served as the foundation for analyzing key themes related to language acquisition, comprehension, learner engagement, and the challenges and successes encountered throughout the process. Central to this inquiry is the concept of Gigi’ihl Algyax ehl Angooga’m—Seeking the spoken language from our Ancestors—which emerged as a guiding principle during the research. I highlight the ways in which curriculum design was adapted to include culturally relevant stories and experiential learning rooted in the lax yip (territory), aligning with Gitxsan ways of knowing. As a Gitxsan educator and daughter of parents who attended Indian Residential School, Day Schools, and Boarding Schools, my work is deeply informed by intergenerational resilience and a commitment to creating safe, supportive learning environments for Indigenous language learners. This research affirms the Gitxsan Ayook of Gwiihl Yee’insxw—the responsibility to pass on knowledge to future generations—and offers insights for educators engaged in Indigenous Language Revitalization. Through storytelling and self-inquiry, this project contributes to broader conversations about decolonizing education and sustaining Indigenous languages within a school setting.Item Unraveling the impacts of the Covid-19 pandemic on mental health among nurses and physicians in Canada(2025) McDonald, Hope; Brousselle, AstridOn March 11, 2020, the World Health Organization declared Covid-19 a Public Health Emergency of International Concern. The pandemic profoundly affected the global population, disrupted society, and had long-lasting effects on Canada’s healthcare system and providers. This project aims to explore and analyze potential factors that impacted the mental health of Canadian nurses and physicians during the Covid-19 pandemic. This project used a sequential mixed-methods design that included a rapid review and semi-structured interviews. The project found that staffing shortages, increased and intensified workload, and a lack of social and administrative support were factors that impacted the mental health of four nurses and four physicians in Canada.