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Sharing of information between health care workers improves evidence dissemination and quality of care. One way to share information is through a community of practice (CoP), whereby members interact regularly towards a common goal. Advances in technology allow CoPs to exist virtually, removing the traditional barriers to information sharing. Virtual CoPs have been shown to be effective, but little is known about why health care workers choose to use them — warranting further investigation. This exploratory research consisted of 86 participants and took place in partnership with Health Quality Ontario. At the time of data collection, the organization was developing a virtual CoP for those in health care to learn from one another about quality improvement. The research utilized the elaboration likelihood model (ELM) — a theoretical model of persuasion that posits that one’s motivation and ability determines how information is processed — to guide the study of attitude formation. ELM distinguishes between Central Route information processing, whereby one is highly motivated and able and pays attention to argument quality, and Peripheral Route processing, whereby lower motivation and ability cause one to be persuaded by peripheral messaging cues. The sustainability of resulting attitudes is influenced by the route through which information is processed. Higher motivation to use a virtual CoP was found to be more strongly correlated to the central route than peripheral route, as expected. Post-hoc analysis found that argument quality had the greatest overall influence on attitudes towards virtual CoPs, regardless of the user’s experience level with them. Users with more experience were also influenced by peripheral cues. The chosen theoretical framework provided insight into the determinants of attitude formation, allowing for a better understanding of how to design and position a virtual CoP for those working in health care — a population yet to be studied through ELM. / Thesis / Master of Science (MSc) / A community of practice (CoP) is a group of people with a shared interest who regularly interact to share knowledge and increase their expertise. Virtual CoPs use information and communications technology to support these knowledge-sharing activities, and have been shown to effectively improve knowledge utilization, but researchers have not examined them from a health care practitioner point of view. The present research aimed to explore the factors affecting how attitudes towards virtual CoPs are formed. The elaboration likelihood model was used to guide this study and suggests that a person’s motivation and ability determine the route through which they process information and form attitudes, leading to their intention to use the system. By understanding what influences attitudes, we can better understand how to design and position a virtual CoP for health care practitioners.

Whenever data is stored or transmitted, it inevitably encounters noise that can lead to harmful corruption. The communication technologies rely on decoding the data using Error Correcting Codes (ECC) that enable the rectification of noise to retrieve the original message. Maximum Likelihood (ML) decoding has proven to be optimally accurate, but it has not been adopted due to the lack of a feasible implementation arising from its computational complexity. It has been established that ML decoding of arbitrary linear codes is a Nondeterministic Polynomial-time (NP) hard problem. As a result, many code-specific decoders have been developed as an approximation of an ML decoder. This code-centric decoding approach leads to a hardware implementation that tightly couples with a specific code structure. Recently proposed Guessing Random Additive Noise Decoding (GRAND) offers a solution by establishing a noise-centric decoding approach, thereby making it a universal ML decoder. Both the soft-detection and hard-detection variants of GRAND have shown to be capacity achieving for any moderate redundancy arbitrary code. This thesis claims that GRAND can be efficiently implemented in hardware with low complexity while offering significantly higher energy efficiency than state-of-the-art code-centric decoders. In addition to being hardware-friendly, GRAND offers high parallelizability that can be chosen according to the throughput requirement making it flexible for a wide range of applications. To support this claim, this thesis presents custom-designed energy-efficient integrated circuits and hardware architectures for the family of GRAND algorithms. The universality of the algorithm is demonstrated through measurements across various codebooks for different channel conditions. Furthermore, we employ the noise recycling technique in both hard-detection and soft-detection scenarios to improve the decoding by exploiting the temporal noise correlations. Using the fabricated chips, we demonstrate that employing noise recycling with GRAND significantly reduces energy and latency, while providing additional gains in decoding performance. Efficient integrated architectures of GRAND will significantly reduce the hardware complexity while future-proofing a device so that it can decode any forthcoming code. The noise-centric decoding approach overcomes the need for code standardization making it adaptable for a wide range of applications. A single GRAND chip can replace all existing decoders, offering competitive decoding performance while also providing significantly higher energy and area efficiency. / 2026-05-23T00:00:00Z

In natural sciences, frequentist paradigm has led statistical practice; however, Bayesian approach has been gaining strength in the last decades. Our study assessed the scalloped hammerhead shark population on the western North Atlantic Ocean using Bayesian methods. This approach allowed incorporate diverse types of errors in the surplus production model and compare the influences of different statistical estimators on the values of the key parameters (r, growth rate; K carrying capacity; depletion, FMSY , fishing levels that would sustain maximum yield; and NMSY, abundance at maximum sustainable yield). Furthermore, we considered multi-levelpriors due to the variety of results on the population growth rate of this species. Our research showed that estimator selection influences the results of the surplus production model and therefore, the value of the target management points. Based on key parameter estimates with uncertainty and Deviance Information Criterion, we suggest that state-space Bayesian models be used for assessing scalloped hammerhead shark or other fish stocks with poor data available. This study found the population was overfished and suffering overfishing. Therefore, based on our research and that there was very low evidence of recovery according with the last data available, we suggest prohibition of fishing for this species because: (1) it is highly depleted (14% of its initial population), (2) the fishery status is very unstable over time, (3) it has a low reproductive rate contributing to a higher risk of overexploitation, and (4) the easiness of misidentification among different hammerhead sharks (smooth, great, scalloped and cryptic species). / Master of Science