Browsing by Author "Powell, Leigh"

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Contextual Conversational Agent to Address Vaccine Hesitancy: Protocol for a Design-Based Research Study
(2022-08) Zidoun, Youness; Kaladhara, Sreelekshmi; Powell, Leigh; Nour, Radwa; Al Suwaidi, Hanan; Zary, Nabil
Abstract: Background: Since the beginning of the COVID-19 pandemic, people have been exposed to misinformation, leading to many myths about SARS-CoV-2 and the vaccines against it. As this situation does not seem to end soon, many authorities and health organizations, including the World Health Organization (WHO), are utilizing conversational agents (CAs) in their fight against it. Although the impact and usage of these novel digital strategies are noticeable, the design of the CAs remains key to their success. Objective: This study describes the use of design-based research (DBR) for contextual CA design to address vaccine hesitancy. In addition, this protocol will examine the impact of DBR on CA design to understand how this iterative process can enhance accuracy and performance. Methods: A DBR methodology will be used for this study. Each phase of analysis, design, and evaluation of each design cycle inform the next one via its outcomes. An anticipated generic strategy will be formed after completing the first iteration. Using multiple research studies, frameworks and theoretical approaches are tested and evaluated through the different design cycles. User perception of the CA will be analyzed or collected by implementing a usability assessment during every evaluation phase using the System Usability Scale. The PARADISE (PARAdigm for Dialogue System Evaluation) method will be adopted to calculate the performance of this text-based CA. Results: Two phases of the first design cycle (design and evaluation) were completed at the time of this writing (April 2022). The research team is currently reviewing the natural-language understanding model as part of the conversation-driven development (CDD) process in preparation for the first pilot intervention, which will conclude the CA’s first design cycle. In addition, conversational data will be analyzed quantitatively and qualitatively as part of the reflection and revision process to inform the subsequent design cycles. This project plans for three rounds of design cycles, resulting in various studies spreading outcomes and conclusions. The results of the first study describing the entire first design cycle are expected to be submitted for publication before the end of 2022. Conclusions: CAs constitute an innovative way of delivering health communication information. However, they are primarily used to contribute to behavioral change or educate people about health issues. Therefore, health chatbots’ impact should be carefully designed to meet outcomes. DBR can help shape a holistic understanding of the process of CA conception. This protocol describes the design of VWise, a contextual CA that aims to address vaccine hesitancy using the DBR methodology. The results of this study will help identify the strengths and flaws of DBR’s application to such innovative projects.
Conversational Agents in Health Education:Protocol for a Scoping Review
(2022) Al Suwaidi, Hanan; Powell, Leigh; Nizam, Mohammed Zayan; Nour, Radwa; Zidoun, Youness; Sleibi, Randa; Warrier, Sreelekshmi Kaladhara; Zary, Nabil
Background: Conversational agents have the ability to reach people through multiple mediums, including the online space, mobile phones, and hardware devices like Alexa and Google Home. Conversational agents provide an engaging method of interaction while making information easier to access. Their emergence into areas related to public health and health education is perhaps unsurprising. While the building of conversational agents is getting more simplified with time, there are still requirements of time and effort. There is also a lack of clarity and consistent terminology regarding what constitutes a conversational agent, how these agents are developed, and the kinds of resources that are needed to develop and sustain them. This lack of clarity creates a daunting task for those seeking to build conversational agents for health education initiatives. Objective: This scoping review aims to identify literature that reports on the design and implementation of conversational agents to promote and educate the public on matters related to health. We will categorize conversational agents in health education in alignment with current classifications and terminology emerging from the marketplace. We will clearly define the variety levels of conversational agents, categorize currently existing agents within these levels, and describe the development models, tools, and resources being used to build conversational agents for health care education purposes. Methods: This scoping review will be conducted by employing the Arksey and O’Malley framework. We will also be adhering to the enhancements and updates proposed by Levac et al and Peters et al. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews will guide the reporting of this scoping review. A systematic search for published and grey literature will be undertaken from the following databases: (1) PubMed, (2) PsychINFO, (3) Embase, (4) Web of Science, (5) SCOPUS, (6) CINAHL, (7) ERIC, (8) MEDLINE, and (9) Google Scholar. Data charting will be done using a structured format. Results: Initial searches of the databases retrieved 1305 results. The results will be presented in the final scoping review in a narrative and illustrative manner. Conclusions: This scoping review will report on conversational agents being used in health education today, and will include categorization of the levels of the agents and report on the kinds of tools, resources, and design and development methods used.
Elements that underpin the design, development and evaluation of social media health interventions. A Scoping Review Protocol
(2021) Nizam, Mohammed Zayan; Powell, Leigh; Zary, Nabil
Background: Social media use has grown tremendously over the years. Given the volume of people on social media and the amount of information being exchanged, it is perhaps unsurprising that social media is being used to promote health interventions. There exists an opportunity for social media-driven health interventions to make a positive impact on health. There is a need to explore the current state of this field, including the platforms being used, models of design, models of behavior change, and evaluation that underpin social media health interventions. This scoping protocol will help to inform those who wish to develop such health interventions. Objective: The main objective of this scoping review protocol is to map the landscape of health interventions disseminated through social media. In addition to which we aim to understand what models of design, models of behavior change, and evaluation underpin social media health interventions. Methods: The methodological framework for this review is guided by Arksey and O’Malley and enhancements by Levac et al. and Peters et al. We will search relevant literature from 9 databases (1) PubMed, (2) PsychINFO, (2) Embase, (4) Web of Science, (5) SCOPUS, (6) CINAHL, (7) ERIC, (8) MEDLINE, (9) Google Scholar. The literature will be screened by at least two reviewers in two stages 1) Title/Abstract screening against the eligibility criteria; eligible articles will then undergo full text screening. Data will be charted using the data charting tool developed by the authors. Results: The results of this study will be presented in the final scoping review in two sections. The first section will describe the search strategy and study selection process and will contain the PRISMA flow chart. The second section will provide key details pertaining to the review objective and question. Conclusions: Our scoping review will provide insights into the use of social media in the field of health intervention. Using social media to drive health interventions is an emerging way of reaching diverse audiences. This scoping review provides an opportunity to explore the current state of the field and help to inform others who wish to enter into the space of social mediadriven health interventions to improve health outcomes.
Feasibility and Educational Value of Clinical Cases Generated Using Large Language Models
(2024-08) Berbenyuk, Anna; Powell, Leigh; Zary, Nabil
Abstract In medical education, case-based learning (CBL) is a fundamental method for training healthcare professionals across different levels of expertise. This approach hinges on using authentic or fabricated clinical cases to bridge the gap between theoretical knowledge and its practical application. It fosters active engagement and knowledge application among learners in healthcare domains. While creating effective cases demands substantial clinical understanding and time investment, the integration of Generative Artificial Intelligence (AI) presents a promising solution to this challenge. AI can efficiently analyze extensive medical data to generate diverse and realistic clinical scenarios, continuously updating case content based on emerging medical literature and guidelines. This study explores AI-generated cases' feasibility and educational value in continuing medical education, focusing on COVID-19 scenarios tailored for the MENA region. Results indicate the potential of AI-generated cases to foster engagement and critical thinking among learners, suggesting their suitability for different levels of education. This study highlights the advantages of integrating AI into CBL and emphasizes the need for future efforts to tackle obstacles and facilitate its successful adoption.
Profiling of Learners in Medical Schools as a Move Toward Precision Education: Protocol for a Scoping Review
(2022) Salman, Hira; Powell, Leigh; Alsuwaidi, Laila; Nair, Bhavana; Tegginmani, Shakeel Ahmed; Mohamadeya, Jalal; Zary, Nabil
Background: Academic experiences seek to get the best out of learners, maximizing performance and developing the skills and competencies needed to foster lifelong learning. The more personalized and tailored the academic experience among learners, the better the outcome. Precision education is a novel approach to research and practice, which is concerned with identifying and tailoring education to the precise needs of the learner. An emerging area of precision education is using data to develop learner profiles for a better understanding of individual learners relative to the characteristics and competencies of lifelong learners. Objective: This scoping review aims to identify literature that reports on profiling learners within medical schools. Our review, as described in this paper, will describe the characteristics being measured, the methods and data sources used to generate profiles, and the resulting profiles that emerge. This review aims to provide guidance to those supporting medical school learners on the current state of learner profiling. Methods: This scoping review will use the Population, Concept, and Context framework, published by Joanna Briggs Institute, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The search strategy was developed in collaboration with a library specialist. An initial search was conducted in PubMed, ERIC, Google Scholar, Cochrane, CINAHL, and SCOPUS. Data will be extracted, and 2 authors will undertake the screening procedure using the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews checklist. Results: The database searches yielded 166 results, and title and abstract screening of 135 extracted articles is currently underway after eliminating 31 duplicates. We anticipate the scoping review to be completed in the first week of October 2022. The final scoping review will present the findings in a narrative and pictorial fashion. Conclusions: This review will help guide scholars looking to understand the current state of learner profiling within medical schools.
A Web-Based Public Health Intervention for Addressing Vaccine Misinformation: Analysis of Learner Engagement and Shift in Hesitancy to Vaccinate
(2023) Powell, Leigh; Nour, Radwa; Al Suwaidi, Hanan; Zary, Nabil
Abstract: Web-based public health interventions can be a useful tool for disseminating evidence-based information to the public. However, completion rates are traditionally low, and misinformation often travels at a faster pace than evidencebased sources. This study describes the design of a web-based public health intervention to address COVID-19 vaccine hesitancy. A quasi-experimental approach was used in which a validated instrument, the Adult Vaccine Hesitancy Survey, was given to learners both pre and post intervention to observe any change in attitude towards vaccination. Our pilot observed a small positive shift in vaccine hesitancy and experienced higher than average completion rates. By integrating motivational learning design into public health interventions we increase the likelihood that learners finish the entire intervention, creating greater chance for positive behavior change.
A Web-Based Public Health Intervention for Addressing Vaccine Misinformation: Protocol for Analyzing Learner Engagement and Impacts on the Hesitancy to Vaccinate
(2022) Powell, Leigh; Nour, Radwa; Zidoun, Youness; Kaladhara, Sreelekshmi; Al Suwaidi, Hanan; Zary, Nabil
Background: A barrier to successful COVID-19 vaccine campaigns is the ongoing misinformation pandemic, or infodemic, which is contributing to vaccine hesitancy. Web-based population health interventions have been shown to impact health behaviors positively. For web-based interventions to be successful, they must use effective learning design strategies that seek to address known issues with learner engagement and retention. To know if an intervention successfully addresses vaccine hesitancy, there must be some embedded measure for comparing learners preintervention and postintervention. Objective: This protocol aims to describe a study on the effectiveness of a web-based population health intervention that is designed to address vaccine misinformation and hesitancy. The study will examine learner analytics to understand what aspects of the learning design for the intervention were effective and implement a validated instrument—the Adult Vaccine Hesitancy Scale—to measure if any changes in vaccine hesitancy were observed preintervention and postintervention. Methods: We developed a fully web-based population health intervention to help learners identify misinformation concerning COVID-19 and share the science behind vaccinations. Intervention development involves using a design-based research approach to output more effective interventions in which data can be analyzed to improve future health interventions. The study will use a quasi-experimental design in which a pre-post survey will be provided and compared statistically. Learning analytics will also be generated based on the engagement and retention data collected through the intervention to understand what aspects of our learning design are effective. Results: The web-based intervention was released to the public in September 2021, and data collection is ongoing. No external marketing or advertising has been done to market the course, making our current population of 486 participants our pilot study population. An analysis of this initial population will enable the revision of the intervention, which will then be marketed to a broader audience. Study outcomes are expected to be published by August 2022. We anticipate the release of the revised intervention by May 2022. Conclusions: Disseminating accurate information to the public during pandemic situations is vital to contributing to positive health outcomes, such as those among people getting vaccinated. Web-based interventions are valuable, as they can reach people anytime and anywhere. However, web-based interventions must use sound learning design to help incentivize engagement and motivate learners to learn and must provide a means of evaluating the intervention to determine its impact. Our study will examine both the learning design and the effectiveness of the intervention by using the analytics collected within the intervention and a statistical analysis of a validated instrument to determine if learners had a change in vaccine hesitancy as a result of what they learned.