Frontlines and Crossroads: The Impact of COVID-19 on the Motivations of Medical Students from Selected Philippine Medical Schools in Pursuing Their Medical Studies

Background: Because the Coronavirus disease 2019 (COVID-19) pandemic forced Philippine medical education to shift online, the loss of practical skills and face-to-face clinical interactions affected many students to cope with lifestyle and learning changes. This study aimed to assess the strength and nature of motivations of medical students to pursue their studies during the pandemic, and to propose recommendations to support them through the pandemic and beyond. Methods: Inductive thematic analysis was done of semi-structured interviews with 17 medical students selected through purposive convenience, purposive and stratified sampling. Recruitment was carried out through the Association of Philippine Medical Colleges. Eligible respondents were pre-clinical and clinical medical students enrolled in School Year (SY) 2020-2021 who experienced the transition to an online setting. Results: The desire to serve motivated most pre-clinical medical students, while financial reward was a factor for clinical medical students. Despite the limitations of online education, lack of social interaction and skills training, medical students had strengthened motivations to continue as the pandemic highlighted the need for physicians, reinforcing their intrinsic desire to serve others despite mental health and financial issues. Conclusions: While most medical students felt more motivated in pursuing their studies during the pandemic, there was a desire and call for more support in their studies and training. Their personal stories suggested there is room for improvement in certain aspects of local medical education. Addressing concerns through financial and educational support, and bridging clinical skills with online learning would help create quality healthcare beyond the pandemic context

Algebraic Adversarial Attacks on Integrated Gradients

Adversarial attacks on explainability models have drastic consequences when explanations are used to understand the reasoning of neural networks in safety critical systems. Path methods are one such class of attribution methods susceptible to adversarial attacks. Adversarial learning is typically phrased as a constrained optimisation problem. In this work, we propose algebraic adversarial examples and study the conditions under which one can generate adversarial examples for integrated gradients. Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples

Frontlines and Crossroads: The Impact of COVID-19 on the Motivations of Medical Students from Selected Philippine Medical Schools in Pursuing Their Medical Studies

BACKGROUND: The onset of the COVID-19 pandemic made changes to the Philippine medical education system to transfer to an online setting, which meant the loss of practical skills needed for future clinical encounters. Most students consider the desire to serve others as their motivation to pursue medicine, but stated that online learning was more favorable for theoretical lessons and not for practical skills. These students had to cope with lifestyle changes which challenged their resolve, as well as deal with the technology and infrastructure required for online learning. The study aimed to compare the strength and nature of motivations of medical students to pursue their studies before and after the onset of the pandemic, and to propose recommendations to support them through the pandemic and beyond. METHODS: Semi-structured interviews were conducted using a study questionnaire that dealt with determining respondents’ motivations for pursuing a medical degree, the impact of the pandemic and how it may have changed their motivations, and what recommendations they can propose to motivate other medical students. Interview transcripts were then analyzed through a qualitative inductive thematic analysis. Pre-clinical and clinical students (clerks and postgraduate interns) enrolled during SY 2020-2021 from Metro Manila and from among the 3 major Philippine island groups were selected to approximate the distribution of medical schools across the country. A total of 17 eligible participants were selected through purposive sampling of different personal backgrounds. Recruitment and call for participants were coursed through the Association of Philippine Medical Colleges - Student Network as well as through social media. Data from interview transcripts were familiarized and ideas from important recurring patterns shared among respondents’ answers were made into codes, which were subsequently organized into themes both unique and generalizable across student groups. RESULTS: Six major themes arose: 1) Contextualizing the pre-clinical and clinical experiences, 2) Challenges of online learning; 3) Desire for lived experience; 4) Tensions between personal contexts and online learning; 5) Grit driven by a desire to serve; 6) Resilience over adversity and sunk cost. Most pre-clinical students were motivated to pursue medicine by a desire to serve, while clinical students were straightforward about financial reward as motivation. Despite the limitations of online education, lack of social interaction and skills training, medical students had strengthened motivations to continue their studies as the pandemic highlighted the need for physicians, reinforcing their intrinsic desire to serve others. Mental health and financial issues were a concern for some, but these students did not wish to quit so as not to lose momentum with their studies. CONCLUSION: While most medical students interviewed felt more motivated in pursuing their studies during the pandemic, there was a desire and a call for more support in their studies and training. Their personal stories suggested there is room for improvement in certain aspects of local medical education. Addressing their concerns through financial and educational support, and bridging clinical skills with online learning would thus help them create quality healthcare in the pandemic context and beyond

A Factored Relevance Model for Contextual Point-of-Interest Recommendation

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines

Verification of systems-on-chips using genetic evolutionary test techniques from a software applications perspective.

This thesis examines verification of system-on-a-chip (SoC) designs using a software applications test methodology that is enhanced by genetic evolutionary test generations and functional coverage. The verification methodology facilitates application based testing using behavioural simulations before the chip is fabricated. The goal of the methodology is to verify commonly used real-life functionalities of the SoC earlier in the design process, so as to uncover design bugs that are considered most critical to actual SoC usages when the SoC is employed in its intended end-product. The verification methodology is based on a test building blocks approach, whereby many different components of various SoC application use-cases are extracted into building blocks, and then recomposed with other components to construct greater variety and range of test cases for verifying the SoC. An important facet of the methodology is to address automated creation of these software application test cases in an effective and efficient manner. The goal is to maximise test coverage and hence bug detection likelihood using minimal verification resources and effort. To this end, test generations techniques employing single and multi objective genetic algorithms and evolutionary strategies are devised in this thesis. Using coverage and test size to drive test generations, test suites which are continually evolved to enhance SoC verifications are created, thereby achieving automated coverage driven verifications. Another enhancement for test generation is to select the input test creation parameters in an analytical manner. A technique using Markov chains is developed to model and analyse the test generation method, and by doing so, test parameters can be selected to achieve desired verification characteristics and outcomes with greater likelihood. To quantify verification effectiveness, a functional coverage method is formulated. The coverage method monitors attributes of the SoC design during testing. The combinations of attribute values indicate the application functionalities carried out. To address the coverage space explosion phenomenon for such combinatorial methods and facilitate the coverage measurement process, partial order domains and trajectory checking techniques from the formal verification field of symbolic trajectory evaluation are adopted. The contributions of this thesis are a verification platform and associated tool-suite that incorporates the software applications test methodology, algorithmic test generation, and functional coverage techniques.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Perceived values as determinants of in-app purchase intentions in mobile games

Despite the growing use of mobile game applications, revenue in-app mobile game purchases remain relatively small in percentage. Little information is available on the motivation of mobile game players to spend in-game applications. This paper explores the determinants of in-app purchases using the perceived value model in the context of mobile game applications. The study utilized the response of 117 game players belonging to the smartphones-using group age. Linear regression was used to determine the relationships of perceived values (playfulness, access flexibility, connectedness, good price, and value for money) to in-app purchase intentions. Results showed that only the rewards derived by mobile game players from playing the game were the only factor positively affecting in-app purchase intentions. Future studies can further explore other factors affecting the motivation of mobile game players in purchasing in-app

Markov modelling and parameterisation of genetic evolutionary test generations

Genetic algorithm, Parameter selection, Markov model, Hardware design verification,