Virtual Posters
Asynchronous Session
Navigating Care: Child Life Therapeutic Language vs. AI in Pediatric Settings
Poster Session Holly Kihm
Child Life Specialists are health care professionals specially trained to provide support for pediatric patients and their families through the use of therapeutic play, diagnostic education, and age-appropriate procedural and surgical preparation. They extensively study child and adolescent development, in particular how to effectively communicate and ensure language used with patients is age and developmentally appropriate, soft, and conveys the right messages at the right time. With the advent and rapid development of artificial intelligence (AI) and its ability to transform concepts and language, it is important for health care providers to identify and understand the subtle and not so subtle differences in how AI would “speak” with patients and when AI may be used as a supplemental means of communication. A variety of AI sources were identified. A standard scenario was entered along with instructions for the AI platform to develop a script, depending on “who” the script was written for, including a patient’s diagnosis. A comparison of the AI generated scripts were made, and a separate comparison of the AI scripts with child life specialist scripts were made. While AI, across several platforms, generated useful scripts, they all missed one important component, the human factor and the ability of child life specialists to consider external factors when communicating with their patients. It is important to remember that communication is an art form, and while AI is honing its language every minute, child life specialists should continue to rely on their own communication skills when working with their patients.
The Genesis of the Fourth-generation Multi-theory Model of Health Behavior Change in Health Promotion View Digital Media
Poster Session Manoj Sharma
The field of health promotion research began in the 1960s with knowledge-based interventions that were derived from knowledge attitude practices (KAP) surveys. These were the first-generation interventions. In the 1970s and 1980s, second-generation interventions that emphasized the development of skills such as cardio-pulmonary resuscitation, first-aid, etc. became popular. These were followed by third-generation evidence-based interventions in the 1990s and 2000s that utilized behavioral theories and focused on acquiring healthy behaviors such as those based on social cognitive theory, theory of reasoned action, theory of planned behavior, etc. In recent years, fourth-generation multi-theory interventions that are brief and precise have gained popularity in the field of health promotion research. One such framework developed in 2015 is the multi-theory model (MTM) of health behavior change. This model is about behavior change as opposed to mere behavior acquisition, is exclusively developed for the field of health promotion, imbibes modifiable and empirically tested constructs from previous theories, is parsimonious, caters to both short-term and long-term behavior change, and is applicable across cultures. The presentation shares this model and its applications using qualitative, cross-sectional, and experimental designs from around the world based on a systematic review, and its future directions in health promotion research.
From a Reductionistic Biomedical Model to a Holistic Healthcare Model: A Paradigm Shift View Digital Media
Poster Session James A. Marcum
In the twenty-first century, medicine and the healthcare system in general are undergoing a paradigm shift in their clinical gaze towards the patient. The shift is from a biomedical gaze that reduces the patient simply to big datasets to a holistic gaze that includes not just the patient’s personal data and narrative but also empowers the patient to participate in the healing or therapeutic process. In this study, two elements are examined in detail that are involved in this shift. The first is artificial intelligence, which provides clinicians with the computing power to make precise clinical decisions on how best to treat the patient based on big datasets. The second element is emotional intelligence, which allows the clinician to gaze empathically on the patient in terms of how the illness disrupts the patient’s life world, especially in terms of the patient’s values and preferences. The benefit of this shift from a reductionist biomedical model to holistic healthcare model is that artificial and emotional intelligence complement one another to provide patients with quality healthcare.
