The Artificial Facilitator: Guiding Participants in Developing Causal Maps Using Voice-Activated Technologies

Reddy, Thrishma; Giabbanelli, Philippe J.; Mago, Vijay

doi:10.1007/978-3-030-22419-6_9

Cited by 16 publications

(5 citation statements)

References 48 publications

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“…In the first step of constructing an FCM, the modeler must identify all the determinants (i.e., nodes) relevant to the problem domain. This can be achieved either by directly collecting relevant information from the stakeholders (e.g., domain experts, community members) or by obtaining such information indirectly from secondary sources (e.g., empirical papers, reports, archives, theories) [ 24 , 25 ]. Indirect approaches can be applied when there are considerable constraints imposed on the project (e.g., hard to reach population, limited resources) or when the problem domain is well covered in secondary sources.…”

Section: Methodsmentioning

confidence: 99%

How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake

Mkhitaryan,

Giabbanelli,

Wozniak

et al. 2023

BMC Public Health

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Background Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. Objective Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. Methods A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. Results Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. Conclusions Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.

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Section: Methodsmentioning

confidence: 99%

How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake

Mkhitaryan,

Giabbanelli,

Wozniak

et al. 2023

BMC Public Health

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“…The 3 knowledge generation step is often the most detailed in studies and also the most time-consuming since it is process-intensive. Participants could be engaged individually via semi-structured interviews (e.g., in person or remotely, through a facilitator or a device [60]) or as a group through workshop activities such as writing factors on post-it notes, consolidating them and linking them collaboratively [47]. Each approach needs a specific skill set from the facilitator; for instance, workshops require paying particular attention to social and group dynamics such that there is equity among participants.…”

Section: Background: a Primer On Participatory Modeling For Causal Ma...mentioning

confidence: 99%

Leveraging Artificial Intelligence and Participatory Modeling to Support Paradigm Shifts in Public Health: An Application to Obesity and Evidence-Based Policymaking

Giabbanelli,

MacEwan

2024

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The Provincial Health Services Authority (PHSA) of British Columbia suggested that a paradigm shift from weight to well-being could address the unintended consequences of focusing on obesity and improve the outcomes of efforts to address the challenges facing both individuals and our healthcare system. In this paper, we jointly used artificial intelligence (AI) and participatory modeling to examine the possible consequences of this paradigm shift. Specifically, we created a conceptual map with 19 experts to understand how obesity and physical and mental well-being connect to each other and other factors. Three analyses were performed. First, we analyzed the factors that directly connect to obesity and well-being, both in terms of causes and consequences. Second, we created a reduced version of the map and examined the connections between categories of factors (e.g., food production, and physiology). Third, we explored the themes in the interviews when discussing either well-being or obesity. Our results show that obesity was viewed from a medical perspective as a problem, whereas well-being led to broad and diverse solution-oriented themes. In particular, we found that taking a well-being perspective can be more comprehensive without losing the relevance of the physiological aspects that an obesity-centric perspective focuses on.

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“…This positive association entails that effective policy response is the ultimate reflection of the state to consider these measures. (Reddy et al, 2019). This approach aims to deliver these tactics and participate in some management facility that evaluates the representation in the setting of organizational alteration.…”

Section: Pearson Correlation and Descriptive Analysismentioning

confidence: 99%

Factors Affecting Artificial Intelligence and Management of Institutional Response to the Event of Coronavirus in Pakistan

Sumra¹,

Noor²,

MehtabAlam³

et al. 2022

JSSH

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With millions of people segregating around the globe, Coronavirus stands truly a global event. It ranges to the trajectories of states with miserable and wrecked health care systems. The transmission is aided by the wide-ranging response from the policy planning and state organizations. Experts are aware of the sternness and contamination of the infectious disease and its disastrous consequences that desire for inoculation of Artificial Intelligence (AI). The absence of an AI policy rejoinder may lead to increased fatalities for weathering the storm. Despite the wide range of responses, the up-to-date policy needs an organized way to track the inflexibility of state-run organizations’ frameworks to attain the objectives of AI organizational policy response. The study’s objectives include including key national institutions to understand perceptions and motivations to challenge the event of COVID-19 through common grounds of Artificial Intelligence. The data is obtained through an online survey from the foreign office, health care services, inter-coordination ministries, and science and technology ministry. The paper has unfolded the useless directions, impractical steps, uncertainty, ineffective communication, and social protection, which led to the rapid spread of infection. Refining each health indicator and reducing the progression of the pandemic through the AI archetype is conceivable only when officialdoms employ the AI-based approach.

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The Artificial Facilitator: Guiding Participants in Developing Causal Maps Using Voice-Activated Technologies

Cited by 16 publications

References 48 publications

How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake

How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake

Leveraging Artificial Intelligence and Participatory Modeling to Support Paradigm Shifts in Public Health: An Application to Obesity and Evidence-Based Policymaking

Factors Affecting Artificial Intelligence and Management of Institutional Response to the Event of Coronavirus in Pakistan

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