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Now showing 1 - 10 of 19

Recent Submissions

Access status: Open Access ,
A gaussian field approach to generating spatial age length keys
(Elsevier, 2021-08) Babyn, Jonathan; Varkey, Divya; Regular, Paul; Ings, Danny; Mills Flemming, Joanna
Access status: Open Access ,
COMPARING PERCEIVED AND UNMET MENTAL HEALTH NEEDS AMONG LGB AND HETEROSEXUAL INDIVIDUALS IN CANADA
(2025-10-09) Prince, S M Kawser Zafor; Not Applicable; Master of Science; Department of Community Health & Epidemiology; Not Applicable; n/a; Not Applicable; Cindy Feng; Jennifer Lane; JianLi Wang
Objectives: This study aimed to (1) estimate and compare proportions of perceived and unmet mental health needs among Lesbian Gay, Bisexual (LGB) and heterosexual individuals in Canada; (2) assess changes in these needs between 2012 and 2022; and (3) evaluate associations between LGB identity and mental health needs. Methods: Data derived from Canadian Community Health Survey - Mental Health (CCHS-MH) 2012 and Mental Health and Access to Care Survey (MHACS) 2022. Survey-weighted proportions were calculated, and multivariable logistic regression examined associations between LGB status and perceived or unmet needs, adjusting for covariates. Effect modification by age, sex, and race was tested. Results: LGB adults reported higher perceived needs in 2012 (44% vs. 17%) and 2022 (62% vs. 23%) (p < 0.05). Unmet needs remained constant at 50%. Adjusted ORs for perceived need were 2.17 (2012) and 2.88 (2022). Unmet needs were significantly higher in 2012 (OR: 1.53) but not in 2022 (OR: 1.09). No effect modification was observed. Conclusions: Despite progress among heterosexual individuals, unmet needs among LGB adults remain unchanged, highlighting the urgency for tailored, trauma-informed, sexual minority-affirming mental health interventions.
Access status: Open Access ,
A Time Segmentation Approach for Estimating Time-Varying Parameters in Northern Fur Seals
(2025-10-07) Alghamdi, Omar Ahmad; No; Doctor of Philosophy; Department of Mathematics & Statistics - Statistics Division; Not Applicable; Dr. Haroon Mouhamed Barakat; No; Dr. Bruce Smith; Dr. Théo Michelot; Dr. Ammar Sarhan
Understanding marine animal behaviour is essential for ecological studies and conservation. Traditional models assume animals switch between a few fixed behavioural states, but behaviour often changes gradually, requiring more flexible methods. This thesis introduces a time segmentation framework for estimating time-varying behavioural parameters from high-resolution northern fur seal tracking data. The approach divides trajectories into short, overlapping windows and applies simple statistical models to estimate movement persistence and variability, enabling dynamic behavioural classification without predefined states. Three studies demonstrate its flexibility: a state-space model with a Kalman Filter for horizontal movement, autoregressive spectral analysis for vertical diving patterns, and a three-dimensional continuous-time model incorporating ocean drift. The framework identifies multiple behavioural modes while avoiding the complexity of switching models, providing interpretable, time-resolved insights into animal movement and offering a practical foundation for future research in movement ecology.
Access status: Open Access ,
TRANSLOCATIONAL MOBILITY: RACIALIZED IMMIGRANT WOMEN'S EXPERIENCES WITH RURAL HEALTHCARE IN NOVA SCOTIA
(2025-10-08) Adisaputri, Gianisa; Not Applicable; Doctor of Philosophy; School of Social Work; Received; Dr. Barathi Sethi; Not Applicable; Dr. Jonathan Amoyaw; Dr. Jude Kornelsen; Dr. Michael Ungar
This study explores the healthcare experiences of immigrant women in rural Nova Scotia, where their access to services is shaped by geographic, social, cultural and structural factors. Employing Constructivist Grounded Theory, the study utilizes in-depth interviews with racialized immigrant women living in rural areas across the province to investigate the gap between the healthcare system access and their expectations and lived experiences. The findings are categorized into three main components. Firstly, the findings reported challenges in racialized immigrant women’s healthcare experiences, including limited service availability, difficulties accessing resources, and their interactions with healthcare providers. Secondly, it revealed the variations of experiences based on social categories, which were further complicated by healthcare and immigration policies, as well as conflicting health and healthcare norms. Thirdly, the findings defined a theory of racialized immigrant women’s continuous efforts to navigate the tensions and contradictions within the healthcare system. This included translocational mobility—a dynamic process of social positioning—where they moved between asserting their belonging within the Canadian healthcare system and society and resisting the exclusionary practices through their differences. This study highlights the need for relational and responsive healthcare that addresses structural inequities in rural health settings.
Access status: Open Access ,
CEREBELLUM ISOLATION WITH MULTI-MODALITY MRI IMAGES USING 3D Unet
(2025-10-07) Li, Yao; Not Applicable; Master of Computer Science; Faculty of Computer Science; NA; Not Applicable; Dr. Evangelos E. Milios; Dr. Janarthanan Rajendran; Dr. Carlos R. Hernandez Castill
The cerebellum plays a crucial role in human life. It is involved not only in motor control but also in various cognitive functions. Understanding how the cerebellum grows and develops throughout the human lifespan helps researchers better comprehend its role in the nervous system and aids in diagnosing related diseases. The first step in analyzing the cerebellum is to isolate it from whole-brain images. This step is critical because any errors at this stage can affect downstream analysis, such as volumetric measurements or morphological comparisons. Over the years, researchers have developed various tools for this task, but current methods face two key limitations. First, most tools are based on templates derived from healthy adults, which are not suitable for other age groups, particularly neonates. Second, many existing tools rely on a single MRI modality (typically T1-weighted images), which limits their ability to accurately distinguish boundaries, especially in regions where contrasts are subtle or noisy. In contrast, multi-modal MRI data (e.g., T1-weighted and T2-weighted images) provide complementary information that can enhance tissue differentiation and segmentation accuracy. This project aims to develop a robust deep learning model capable of distinguishing the cerebellum from surrounding brain structures. We employ a 3D U-Net architecture, which has proven effective in extracting features from medical images. The U-Net model is designed to capture both high-level semantic features and low-level spatial details, making it ideal for distinguishing the cerebellum from nearby brain structures. Our model takes as input two channels of cropped cerebellar regions from multi-modal whole-brain MRI scans. We evaluate model performance using Dice Score and Hausdorff Distance. Compared to existing tools, our model achieves higher accuracy. In particular, the lower Hausdorff Distance indicates a reduced likelihood of mislabeling surrounding brain structures as cerebellum. Moreover, our model shows its capacity to process images from different age groups.
Access status: Open Access ,
Validation of close‐kin mark–recapture (CKMR) methods for estimating population abundance
(Wiley, 2019-07) Ruzzante, Daniel E.; McCracken, Gregory R.; Førland, Brage; MacMillan, John; Notte, Daniela; Buhariwalla, Colin; Mills Flemming, Joanna; Skaug, Hans