DalSpace Institutional Repository

DalSpace is a digital service that collects, preserves, and distributes digital material produced by the Dalhousie community.

To learn about content guidelines, policies, and how to deposit, view the Help documents.
Contact us to get started submitting content to DalSpace at dalspace@dal.ca

Communities in DSpace

Select a community to browse its collections.

Now showing 1 - 10 of 19

Recent Submissions

Access status: Open Access ,
In Memoriam: Dr. Edwin (Eddie) Michael Rosenberg
(Dalhousie University, 2025)
Access status: Open Access ,
In Memoriam: Dr. Edwin "Ted" Roy Luther
(Dalhousie University, 2025)
Access status: Open Access ,
In Memoriam: Dr. Jacques Derosiers
(Dalhousie University, 2025)
Access status: Open Access ,
PostMom: An AI-Powered Culturally Tailored Persuasive App for Postnatal Support for Underserved Nigerian Mothers
(2025-12-24) Ogbonnaya-Okafor, Chinenye; Not Applicable; Master of Computer Science; Faculty of Computer Science; Received; N/a; Yes; Dr. Lizbeth Escobedo Bravo; Dr. Oladapo Oyebode; Dr. Rita Orji
The World Health Organization (WHO) recommends that pregnancy, childbirth, and the postnatal period should be positive experiences for all women, yet many underserved women in countries including Nigeria still lack adequate postnatal support. Limited access to postpartum care, cultural barriers, and inadequate health communication continue to hinder positive postnatal outcomes for Nigerian mothers. Although artificial intelligence (AI)-powered mobile health (mHealth) applications exist for maternal health, culturally appropriate interventions that integrate persuasive strategies for postnatal education and support remain limited, especially in low-resource contexts. This thesis presents PostMom, a persuasive mHealth technology that integrates AI techniques, including natural language processing (NLP), to deliver culturally appropriate postnatal health education and support for underserved Nigerian mothers. This work followed a three-phase, user-centered process. First, a review of 62 studies on AI in maternal health identified gaps in existing solutions and informed a set of persuasive strategies using the Persuasive System Design (PSD) model. Second, guided by these insights and consultations with four Nigerian medical doctors, a medium-fidelity prototype of PostMom was designed and evaluated through surveys and semi-structured interviews with 36 Nigerian mothers, whose feedback shaped design refinements. Third, a refined version of PostMom was developed and deployed in a one-week field study during which 70 mothers used the application for daily postnatal guidance and support. Results from validated usability and user experience assessments demonstrated good usability and positive user experience, while pre-post knowledge assessments showed that PostMom's persuasive features significantly improved participants' postnatal health knowledge (p < .001) and enhanced health information literacy. This thesis offers design recommendations for postnatal health applications, underscoring the importance of a human-in-the-loop design process. Overall, this work contributes to understanding the design and development of culturally tailored, AI-powered persuasive technologies for improving postnatal health knowledge, and enhancing postnatal care experiences for underserved populations.
Access status: Open Access ,
The Prevalence of Substance Use in Injured Off-Road Vehicle Drivers: A Systematic Review and Observational Study
(2025-12-15) Rheault, Grace; Not Applicable; Master of Science; Department of Community Health & Epidemiology; Received; NA; Not Applicable; Dr. Jill Hayden; Dr. Mark Asbridge; Dr. David Clarke
Background: Drug driving has become as common as alcohol-impaired driving in Canada and is a major public health issue. Substance use in the context of off-road vehicle driving (i.e., ATVs, snowmobiles, dirt bikes) has not been well-studied in Canada. The prevalence of acute substance use in injured off-road vehicle drivers across Canada is currently unknown. The aim of this study is to estimate the prevalence of acute substance use among injured off-road vehicle drivers in Canada. Objectives: This thesis includes two main studies and three key objectives. Part 1: Systematic Review and meta-analysis The first study involves a systematic review and meta-analysis, with the purpose of (1) estimating the prevalence of acute substance use in drivers involved in off-road vehicle crashes. Part 2: Observational Study The second study is a primary study of injured off-road vehicle drivers presenting to emergency departments across Canada. The two objectives of this study are: (2) to estimate the prevalence of acute substance use in injured off-road vehicle drivers presenting to emergency departments in Canada; and (3) to determine what factors (i.e. demographic, crash characteristics, regional variation) are associated with acute substance use in injured off road vehicle drivers presenting to emergency departments in Canada. Systematic Review Methods: Studies were identified from electronic databases and grey literature. Study selection, data extraction, and risk of bias assessment were completed by two independent reviewers. Descriptive statistics were used to report study characteristics. Random effects meta-analyses were used to report the overall pooled prevalence, and subgroup and sensitivity analyses were used to explore study characteristic effects and the robustness of results. Primary Study Methods: Employing data from the National Drug Driving Study, this study analyzed data from injured drivers presenting to 18 participating EDs across Canada who were in an off-road vehicle crash and had blood drawn for clinical purposes. Excess blood from clinical use underwent toxicology analysis to quantify the presence of impairing substances in the injured driver. Prevalence estimates of substance use in injured off-road vehicle drivers were reported overall, by substance type, by level of alcohol and tetrahydrocannabinol (THC), by number of substances and disaggregated by relevant covariates. Results: 20 publications comprising of 18 studies were included for review, with 2 sets of publications that had overlapping data, drawn from the same studies. Prevalence estimates ranged from 8% to 85%. The overall pooled prevalence estimate of substance use was 41% (95% CI: 28%-54%). There was significant heterogeneity across studies (Q = 3456.38, p < 0.001; I2=99.4%). Subgroup analyses found significant differences between studies using different data sources (coroner’s data: 57% (43%-71%) vs. hospital data (29% (6%-58%), and between non-fatally (29%, 95% CI: 9%-55%) and fatally injured drivers (56%, 95% CI: 43%-69%). The observational study included 473 injured drivers who met the inclusion criteria. Overall, 71% of tested drivers were positive for at least one impairing substance. Alcohol was detected in 38% of drivers, and 69% of those drivers were above the legal blood alcohol concentration (BAC) limit of 0.08% BAC. THC was detected in 17% of drivers. Central nervous system (CNS) depressants were detected in 31%, opioids in 14%, and CNS stimulants in 13% of drivers. About a third (32%) of drivers tested positive for more than one substance. Conclusion: These findings reveal that a large proportion of injured off-road vehicle drivers test positive for substances, which indicates a serious public health issue. Targeted interventions for at-risk populations and increased law enforcement are required to reduce impaired driving among off-road vehicle drivers.
Access status: Open Access ,
UNSOURCED RANDOM ACCESS WITH USER LOCALIZATION AND SENSING
(2025-12-14) Soltani, Roshanak; Yes; Doctor of Philosophy; Department of Electrical & Computer Engineering; Not Applicable; Dr. Ekram Hossain; Yes; Dr. Jason Gu; Dr. Alex Brodsky; Dr. Dmitry Trukhachev
Future wireless networks must support massive numbers of sporadic transmissions while meeting strict spectrum and energy limits. Unsourced random access (URA) provides a grant-free model in which devices transmit without pre-allocated resources and user identifiers and the receiver jointly detects user activity and decodes messages from a large number of users. This thesis advances URA by enabling sensing and localization within the communication process to build scalable frameworks that remain reliable and energy-efficient under heavy loads. It also contributes URA designs that tolerate relaxed synchronization and support longer payloads when needed. Amulti-antenna preamble–payloadURAarchitecture is developed that fuses compressedsensing (CS)-based activity detection with iterative multiuser detection (MUD), decoding, and channel refinement on Rayleigh fading channels. The design is extended to lowscattering regimes through channel models that account for array geometry, consistent with high-frequency environments. Building on this foundation, the thesis applies integrated sensing and communication (ISAC) principles. A location-based URA framework is introduced in which the base station (BS) localizes active users while detecting and decoding their data using only uplink signals. The method ties user transmission features to angle-ofarrival (AoA) and partitions space into sectors with sub-pool allocation and reuse to reduce collisions and complexity. Localization improves communication by providing spatial information that helps separate multiple packets. The concept is further generalized in the random-access procedure. The thesis also demonstrates environment sensing where user uplink signals act as opportunistic illuminators for multi-object sensing at the BS, revealing the synergy and trade-offs between sensing and communication. Some structural challenges of URA are also addressed. Scalability under timing uncertainty is achieved through a fully asynchronous URA design that removes slot and beacon requirements and performs sliding window timing acquisition together with MUD on the Gaussian channel. For longer messages a multi-segment URA structure is proposed that stitches decoded segments across consecutive slots and employs a dual preamble pool to identify the first segment and reduce collisions. Together, these studies show how URA can jointly deliver reliable data detection, user localization, and environmental sensing with high scalability, paving the way for intelligent and spectrum efficient random-access in future networks.