ABSTRACT
Panic buying occurs when unusually excess amounts of goods are bought in an anticipation of a crisis, perceived crisis, or in the aftermath of a crisis. Especially during the ongoing COVID-19 crisis, it was influenced by individuals' threat perception, fear of uncertainty, maladaptive coping, and social modeling. Artificial intelligence (AI) is an ever-evolving field, and its role in mental health has been widely studied. The traditional aspects of AI, namely, probability, linguistics, learning, reasoning, knowledge representation, and perception, may all be helpful in targeting various correlates of panic buying. Even though literature on the use of AI and machine learning to prevent panic buying is very limited, the existing models in healthcare can be extrapolated to that effect. Predicting buying patterns during crisis, personalizing supplies, warning signals for optimal threshold of buying, surveillance in markets, and ensuring enough resources of essential items are some of the areas that can be helped by AI. However, specific research, understanding, funding, standardization, and technical optimization are needed in this area before the promising field of AI helps prevent panic buying. This chapter provides a bird's-eye view related to the intersections of AI and panic buying as well as the directions ahead. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Panic buying occurs when unusually excess amounts of goods are bought in an anticipation of a crisis, perceived crisis, or in the aftermath of a crisis. Especially during the ongoing COVID-19 crisis, it was influenced by individuals' threat perception, fear of uncertainty, maladaptive coping, and social modeling. Artificial intelligence (AI) is an ever-evolving field, and its role in mental health has been widely studied. The traditional aspects of AI, namely, probability, linguistics, learning, reasoning, knowledge representation, and perception, may all be helpful in targeting various correlates of panic buying. Even though literature on the use of AI and machine learning to prevent panic buying is very limited, the existing models in healthcare can be extrapolated to that effect. Predicting buying patterns during crisis, personalizing supplies, warning signals for optimal threshold of buying, surveillance in markets, and ensuring enough resources of essential items are some of the areas that can be helped by AI. However, specific research, understanding, funding, standardization, and technical optimization are needed in this area before the promising field of AI helps prevent panic buying. This chapter provides a bird's-eye view related to the intersections of AI and panic buying as well as the directions ahead. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
BACKGROUND: Virtual clinical interactions have increased tremendously since the onset of the COVID-19 pandemic. While they certainly have their advantages, there also exist potential limitations, for example, in establishing a therapeutic alliance, discussing complex clinical scenarios, etc. This may be due to possible disruptions in the accurate activation of the human mirror neuron system (MNS), a posited physiological template for effective social communication. OBJECTIVE: This study aimed to compare motor resonance, a putative marker of MNS activity, estimated using transcranial magnetic stimulation (TMS) elicited while viewing virtual (video-based) and actual or real (enacted by a person) actions in healthy individuals. We hypothesized that motor resonance will be greater during real compared to virtual action observation. METHODS: We compared motor resonance or motor-evoked potential (MEP) facilitation during the observation of virtual (presented via videos) and real (enacted in person) actions, relative to static image observation in healthy individuals using TMS. The MEP recordings were obtained by 2 single-pulse (neuronal membrane excitability-driven) TMS paradigms of different intensities and 2 paired-pulse (cortical gamma-aminobutyric acid-interneuron-driven) TMS paradigms. RESULTS: This study comprised 64 participants. Using the repeated measures ANOVA, we observed a significant time effect for MEP facilitation from static to virtual and real observation states when recorded using 3 of the 4 TMS paradigms. Post hoc pairwise comparisons with Benjamini-Hochberg false discovery rate correction revealed significant MEP facilitation in both virtual and real observation states relative to static image observation; however, we also observed a significant time effect between the 2 action observation states (real > virtual) with 2 of the 4 TMS paradigms. CONCLUSIONS: Our results indicate that visual cues expressed via both virtual (video) or real (in person) modes elicit physiological responses within the putative MNS, but this effect is more pronounced for actions presented in person. This has relevance to the appropriate implementation of digital health solutions, especially those pertaining to mental health.
ABSTRACT
Loneliness and social isolation are significant public health crises in older adults. The issues about companionship have many psychosocial and cultural dimensions, which is further compounded by the current COVID-19 pandemic. In modern-day India, there is a significant increase in the number of older adults left to live alone because of sociocultural changes in our society. Companionship in late life is known to promote the quality of life and decrease the mental health morbidity. There is an increasing role of pets as companions to the elderly. Novel technologies such as artificial intelligence in the form of robots are being explored to support the elderly. Sexuality is another complex issue related to older adults that is often ignored. The sexuality and sexual functioning in older adults largely depend on physiological, psychological, and sociocultural factors. The principles of ageism have influenced sexuality in older adults. Sociocultural issues and the aging-related pathophysiological changes can contribute to an increased risk for legal issues related to sexuality in this population. There is a need for more systematic research into the multifaceted concept of companionship and sexuality in the older adult population. This review article addresses these two distinct subjects separately.