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From the Roots Up: (Re)Making Indigenous Women and Two-Spirit Peoples’ Relationships with Land
Get PDFDespite the interference caused by waves of colonialism, Indigenous peoples are actively pursuing and preserving relationships with Land and more-than-human-beings. As Indigenous land-based education is relatively new as an academic field of study, it is important to continue to assert clear understandings of the relationships and principles that inform it, and of those that distinguish it from other forms of land-based, environmental or place- based education. It is of primary importance within the field of Indigenous land-based education to fully investigate and analyze the ways in which notions of gender operate within the movement. This dissertation centers the experiences of Indigenous women and Two-Spirit Indigenous land-based educators and practitioners in relation to the research questions: How does the gendering of land-based practices and land-based knowledges impact Indigenous peoples’ relationships with Land? and How can gender inclusive land-based education restore and strengthen these relationships?
Eight interviews with Indigenous women and Two-Spirit land-based educators and practitioners, were conducted and analyzed using the voice centered relational method. In addition to the interviews, a selection of publicly circulated posters and the creative writing work of the author, were analyzed and discussed to demonstrate how the themes presented in the research operate within communities. The analysis revealed that participant experiences engaging in Indigenous land-based practices have been impacted by colonialism resulting in the disconnection of Indigenous land-based knowledges from practices, and thereby negatively impacting Indigenous women and Two-Spirit relatives’ relationships with Land and their ability to engage in land-based practices. Furthermore, in spite of gender based violences and inequities caused by colonial disruptions, Indigenous women and Two-Spirit land-based educators and practitioners resist and refuse colonialism and its manifestations as both a reaction to external forces but an important response in alignment with the ethics of relationality, relational accountability, and relational care
CaregiVR: Building Self-Efficacy In Dementia Care Through Immersive Education
Get PDFBackground: Over the next three decades, the number of older persons is projected to more than double worldwide, reaching more than 1.5 billion in 2050 (World Health Organization, 2019). Globally, the number of adults with dementia could rise from about 57.4 million in 2019 to 152.8 million by 2050 (World Health Organization, 2019). This reflects an increase of 166%. Current dementia education for practical nursing students is mostly reliant on traditional teaching methods including lecture-based courses and clinical placements, with limited opportunity to develop competence through repetition. One example of an emerging technology in nursing education is virtual reality (VR). Virtual reality can provide a level of immersion into a virtual environment, thereby mimicking reality, and providing opportunity for immediate performance feedback and repetition as necessary, providing ongoing, iterative learning.
Increased self-efficacy, the belief in our ability to meet challenges, has been linked to the reduction of stress (Tang & Chan, 2016). Within the nursing student population self-efficacy has been correlated with higher resiliency, leading to improved academic performance and ability to carry out the role of the nurse in the clinical setting (Cuartero & Tur, 2021). While a variety of educational strategies exist to attempt to accomplish improving self-efficacy for nursing students, no studies have been conducted on the use of immersive virtual reality as a potential tool for improving self-efficacy in nursing students for managing aggressive behaviors in clients with dementia.
Methodology: In this project an explanatory sequential mixed-methods design with an interpretive descriptive approach was used to compare perceived self-efficacy for practical nursing students who used the CareGiVR virtual reality application with those who did not. The following research questions were addressed:
(1) Does perceived self-efficacy improve for practical nursing students who use the CareGiVR application compared to those who do not, in relation to managing aggressive behaviors in clients with dementia?
(2) Are there significant differences between practical nursing students’ perceived self-efficacy with managing aggressive behaviors in clients with dementia before and after using the CareGiVR application?
(3) How did practical nursing students perceive using the CareGiVR application influenced their self-efficacy with managing aggressive behaviours in clients with dementia?
Participants were recruited through email invitation and classroom presentations. The Inventory of Geriatric Nursing Self-Efficacy (IGNSE) measured changes in perceived self-efficacy pre and post-intervention, followed by qualitative focus groups.
Results: Forty-six students total (49%) responded to the invitation to participate in the quantitative component. Fifteen students from the intervention group, who utilized the CareGiVR application, elected to participate in the follow-up qualitative focus groups.
Findings indicate participants who used the CareGiVR application reported statistically significant higher levels of perceived self-efficacy post-intervention, compared with their baseline. Compared to the control group, participants who used the CareGiVR application had statistically significant higher levels of perceived self-efficacy following their clinical rotation. Four themes were identified during the qualitative analysis: getting real-world experience, a safe place to practice, meeting the client where they are at, and a tool, not a replacement.
Conclusion: These findings support the use of immersive virtual reality as an effective tool to increase perceived self-efficacy for managing aggressive behaviors in clients with dementia for practical nursing students
An Enduring Story of an Iconic Animal: Archaeology and Bison as Support for Wanuskewin as a UNESCO World Heritage Site
Get PDFBison are a key to every aspect of Wanuskewin Heritage Park. This thesis undertakes a detailed faunal analysis of the remains from Wolf Willow, a multicomponent archaeological site in the Opimihaw Creek Valley. The results of this study show the Plains bison (Bison bison bison) to be the dominant animal in the assemblage. Large quantities of highly fragmented remains indicate area was a habitation space, taphonomic marks indicate the bison were used for food production and tool manufacture. More than any other animal, bison are what people used in their day-to-day life when occupying this space. From this information, the research expands to look at all other sites in the valley area, drawing the conclusion that bison are the most commonly present animal in all archaeology sites and habitation areas in the Opimihaw Creek Valley. They are pervasive in the past occupations. Investigation into other lines of evidence that depict and demonstrate the bison within the Park area is conducted, with Hoofprint Tradition rock art and the presence of bison iconography in archaeological sites.
Today, Plains bison have been returned to restored grassland fields at Wanuskewin. Their presence is for education, restoration, culture, and ceremony. They are a spiritual herd and give a visual for how their presence would have been felt in the past. The bison are a part of every aspect of the story of Wanuskewin. With the archaeological research done, specifically faunal analyses and the remains from Wolf Willow, bison are clearly important. Current efforts from Wanuskewin Heritage Park to become established as a UNESCO World Heritage site have ideas coming up about what makes this space truly special. The second part of this work discusses this, and how so easily every part of the story of Wanuskewin relates back to bison. They are what Wanuskewin is about, past and present, and give it its Outstanding Universal Value
The Weight of Water: Using a Geological Weighing Lysimeter to Quantify the Field-Scale Water Balance
Get PDFQuantifying water and energy fluxes are critical to understand how water is moved and stored on the landscape. These measurements are important for flood and drought forecasting, water resources management, and large-scale numerical weather prediction models. Moreover, land surface model’s (LSMs) which are hydrological tools used to predict and forecast water and energy fluxes, rely on these measurements to calibrate and validate their predictions. To evaluate hydrological fluxes and in turn water storage, representative observations are needed to capture the temporal and spatial dynamics of water on the landscape. However, hydrological fluxes are often difficult to measure and are limited to specific fluxes and spatial resolutions. Geological Weighing Lysimeters (GWL) are novel instruments that provide measurements of total integrated water storage at scales of 102 m2 and 106 m2 (field-scale). These tools use a saturated formations response to changes in mechanical loading, to estimate the change of water storage on the land surface. This research assessed the efficacy of a GWL in a deep confined aquifer at a research site in Duck Lake, Saskatchewan, to measure total water storage and partition individual stores from field-scale water balance. We found when coupled with supplementary observations of shallow groundwater and snow storage, the GWL provided a reliable record of temporal storage dynamics observed in point scale dielectric probes. Inconsistencies in soil moisture storage were from the dielectric probes inability to measure ice content in the soils and different estimates of hydrological fluxes between scales. We then used these storage estimates to critically assess the performance of two LSMs: the Canadian Land Surface Scheme (CLASS) and the Structure for Unifying Multiple Modeling Alternatives: (SUMMA). We found each LSM was able to reproduce total water storage and subsurface storage dynamics well, however they both had major inconsistencies simulating snowpack dynamics and hydrological fluxes. We speculate these inconsistencies are the result of differences in soil hydraulic property representations. The outcome of this research is two-fold. First, GWL and supplementary observations can be used to partition individual storage components from the water balance providing insight into hydrological fluxes; and secondly, small differences in soil hydraulic properties may largely influence Land Surface Schemes (LSSS’s) simulated fluxes, more research is needed to assess the influence have
Machine Learning Approaches for Faster-than-Nyquist (FTN) Signaling Detection
Get PDFThere will be a significant demand on having a fast and reliable wireless communication systems in future. Since bandwidth and bit rate are tightly connected to each other, one approach will be increasing the bandwidth. However, the number of wireless devices are growing exponentially, and we don't have infinite bandwidth to allocate. On the other hand, increasing the bit rate for a given bandwidth, i.e., improving the spectral efficiency (SE), is another promising approach to have a fast and reliable wireless communication systems. Faster-than-Nyquist (FTN) is one of the candidates to improve the SE while this improvement comes at the expense of complexity of removing the introduced inter-symbol interference (ISI). In this thesis, we propose two algorithms to decrease the computational complexity regarding removing the ISI in FTN signaling.
In the first main contribution of the thesis, we introduce an equivalent FTN signaling model based on orthonormal basis pulses to transform the non-orthogonal FTN signaling transmission to an orthogonal transmission carrying real-number constellations. Then we propose a deep learning (DL) based algorithm to decrease the computational complexity of the known list sphere decoding (LSD) algorithm. In essence, the LSD is one of the algorithm that can be used for the detection process of the FTN signaling; however, at huge computational complexity. Simulation results show the proposed DL-based LSD reduces computational complexity by orders of magnitude while maintaining close-to-optimal performance.
In the second main contribution of the thesis, we view the FTN signaling detection problem as a classification problem, where the received FTN signaling signal viewed as an unlabeled class sample that is an element of a set of all potential classes samples. Assuming receiving samples, conventional detectors search over an -dimensional space which is computationally expensive especially for large value of . However, we propose a low-complexity classifier (LCC) that performs the classification in dimensional space where . The proposed LCC's ability to balance performance and complexity is demonstrated by simulation results
The Likelihood of Seeking Information on OTC Medicines
Get PDFOver-the-counter medicines have been reported as safe medicines by Canadians, yet there are concerns about their misuse and adverse drug reactions. Previous evidence shows that users of these medicines obtain information from sources such as professionals, relatives, friends, news and print media, and product labels. However, little is known about how likely and extensively consumers would seek information relative to other consumer products. The study purposed to explore the likelihood and extent of seeking information about OTC medicines relative to common consumer products, as a surrogate of the importance consumers place on such medication.
The study was cross-sectional and descriptive in design. Common consumer products (10 non-drug and five OTC medicines) and 15 OTC medicines were selected for comparative purposes. Saskatchewan residents were asked to rate these products on a scale of 1 to 10 for the likelihood and extent of information-searching and product familiarity. Impressions of effectiveness and safety of OTC medicines were also provided on the same scale. A scale was developed to measure consumer Propensity to Self-Medicate. Test-retest reliability was estimated using Pearson correlation coefficients (r), Intraclass Correlation Coefficients (ICC) and Paired Sample t-tests. Descriptive and inferential analyses of the findings were undertaken.
A total of 575 responses were gathered, for a response rate of 19.2 percent. The mean age of respondents was 63.0 years and 61.6 percent were female.
The likelihood respondents will search for information about OTC medicines had a similar rating to common consumer products, ranking less than televisions, coffee makers, and bike helmets, but more than body lotion, sunglasses, and red wine. Product familiarity ranged from 5.8 for headache medicines down to 3.9 for red wine (10-point scale). A moderate rating was given to the perception of safety and effectiveness for the 15 OTC medicines. Product effectiveness ranged from 7.3 for headache medicines to Athlete’s Foot creams at 5.1. Product safety for adult cough syrups was slightly higher than such products for children. Factors motivating self-medication among respondents are associated with their perception on Purchase Involvement, Self-Efficacy, Awareness of Care Needed during Self-Medication, and Perceived Usefulness of OTC Medications. The tendency to self-medicate was higher among women and the elderly.
Given the potential for OTC medicines to help resolve symptoms, but also do harm, sufficient care must be undertaken when deciding to use one. While the likelihood to search for information on OTC medicines was similar to rather mundane consumer products, it is too early to consider the two groups as possessing similar levels of importance to the public. But it is concerning. There is a plethora of information currently available to consumers on such medicines; motivating them to access it may need attention. The development of a Propensity to Self-Medicate scale was exploratory and novel, and may be relevant in future research for different population settings and context
A pyridinium-modified chitosan-based adsorbent for arsenic removal via a coagulation-like methodology
Get PDFThis article is licensed under a Creative Commons Attribution Non-Commercial 3.0 Unported Licence. © 2023 The Author(s).Government of Canada through the Natural Sciences and Engineering Research Council of Canada (Discovery Grant Number: RGPIN 04315-2021)Peer ReviewedThe goal of this study was to synthesize a chitosan-derived adsorbent that can be used in a coagulation– flocculation (CF) process for facile integration into existing water treatment processes. Therefore, an insoluble pyridinium-modified chitosan (Chi-Py) was prepared. Structural characterization was achieved with spectroscopy (FT-IR, 13C solids NMR, and X-ray photoelectron) methods and thermogravimetric analysis. Approximately 7% di-nitrobenzene and ca. 30% pyridinium moieties were incorporated into the chitosan framework via an adapted, moderate-temperature, Zincke reaction. The arsenic removal efficiency was evaluated by a coagulation-inspired methodology at pH 7.5, where the results were compared against CF systems such as pristine chitosan, FeCl3 and chitosan–FeCl3. The kinetic and van't Hoff thermodynamic parameters for arsenic removal were calculated. Arsenic adsorption was shown to be a spontaneous and exothermic process (ΔG = −4.7 kJ mol−1; ΔH = −75.6 kJ mol−1) with a 76% arsenic removal efficiency at 23 °C and 96% at 5 °C with a maximum effective adsorbent dosage of Chi- Py of 300 mg L−1. The adsorption process for Chi-Py followed pseudo-first order kinetics, where the pyridinium-modified chitosan adsorbent can be successfully employed similar to coagulant-like systems in conventional water treatment processes. In contrast to conventional adsorbents (1–2 g L−1), a dosage of only 300 mg L−1 was required for Chi-Py that offers greater sustainability and recycling of materials. This is contrasted with single-use conventional coagulants such as FeCl3 or binary FeCl3–chitosan CF systems
Giftwrapped Data : Working together on a model-agnostic platform for speeding up predictions for water management
Get PDFCanada First Research Excellence FundNon-Peer ReviewedA young engineer's personal account of collaborating with hydrological modelers to develop a new model-agnostic workflow to expedite data preparation
Both Eyes on the Ice : Investigating a hazard on the Slave River
Get PDFCanada First Research Excellence FundPersonal account from a scientist about learning from his local guide while conducting river ice research in the Canadian Northwest Territories
EXPLORING THE POTENTIAL OF COMPUTER VISION AND MACHINE LEARNING IN ENHANCING THE FUNCTIONALITY OF AN EMG-CONTROLLED PROSTHETIC HAND
No full textThe potential of using machine learning techniques to develop prosthetic arms that can automatically perform hand gestures and grasp objects is very important in healthcare systems. Hands are an important part of the body for all vertebras, animals use theirs for locomotion, however, because of our bipedal nature as humans, we use our hands majorly for gripping and general manipulation. Humans without hands must live with prostheses, as a result, prosthetic hands must be well-sophisticated to perform the functions of a regular human hand. Many electronic prosthetics available in the market come with sophisticated control methods. It may take several months of continuous training for the human to learn how to accurately control the prosthetic fingers and perform tasks like picking up objects. This research aims to alleviate this problem by proposing an automated method for performing hand gestures and grasping objects using computer vision-based techniques and machine learning. This research demonstrates the feasibility of this approach by training tree-based classifiers to interpret EMG signals because they offer a direct measure of feature importance. Of the two tree-based classifiers implemented, the results show that the decision tree classifier outperforms the random forest classifier in terms of precision, recall, and F1-score, with EMG signals from Channel 2 being the most important feature for both models. Using an RGBD camera mounted at the base of the gripper which records observation in discrete steps, this research demonstrated the effectiveness of machine learning in automated object gripping. Agents were trained using the existing Soft Actor-Critic (SAC), Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). This research shows that the SAC algorithm is the most effective approach for training agents to perform automated grasping tasks, outperforming other algorithms such as DQN and PPO in terms of their success rate. The agents were trained on three different types of objects (remote controller, soap bar, and mug), and the results show that the two factors (object shape and size) affect the agent's ability to converge to an optimal policy. The SAC algorithm demonstrated a remarkable resilience when tested on diverse environments and objects, and at varied hyperparameters. While the PPO algorithm demonstrated greater adaptability than DQN, it did not perform as well as the SAC algorithm in terms of overall success rate and ability to handle diverse scenes and objects. Discussions of the reason behind these results are provided. The contribution of this thesis is the conclusion that the second channel of an 8-channel EMG device is the most significant when using Decision Tree Classifiers to interpret EMG signals. Also, the SAC algorithm has a great potential in developing intelligent prosthetic arms with the automatic object gripping capabilities, paving the way for more advanced prosthetics in the future