Yang Yi

Narrative messages are widely recognized for their capacity to shape attitudes, beliefs, and even behavior (Braddock & Dillard, 2016; Shen et al., 2015; Zebregs et al., 2015). This persuasive poten...

Background: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. Objective: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. Methods: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. Results: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. Conclusions: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

Purpose: As the use of generative artificial intelligence (Gen AI) grows in the workplace, organizations prioritize employee training due to ethical concerns. Employing the social-cognitive perspective of moral action, this project examined how organizations’ communication strategies and employees’ ethical orientations collectively impact the Gen AI training outcomes. Design/methodology/approach: An online survey was conducted in February 2024 with 500 full-time employees in the United States whose organizations had implemented any form of Gen AI training. Structural equation modeling (SEM) and Hayes’s PROCESS Model 1 were employed for the data analysis. Findings: The results revealed that change leadership and transparent communication during the training fostered ethical Gen AI adoption. Such enhanced ethical adoption of Gen AI subsequently enhanced employees’ proficient use of this technology. Employees’ ethical orientations (deontology and consequentialism) moderated this effect: those with lower procedural ethics (higher scores in consequentialism and lower scores in deontology) were more likely to be influenced by their leaders’ change-related actions. Originality/value: The study provides theoretical insights and practical advice for utilizing organizations’ efforts to navigate employees’ Gen AI usage at work. Theoretically, the study presents an organizational communication framework connecting leadership, internal communication, ethical readiness, and work empowerment outcomes to guide employees’ ethical decision-making during new implementations and training. Practically, it provides organizations with data-proven Gen AI ethical training suggestions, such as enhancing leaders’ change-specific guidance and building transparent training environments.

BACKGROUND Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. OBJECTIVE We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. METHODS We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. RESULTS Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. CONCLUSIONS Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

Scholars have gradually shifted efforts to understand the COVID‐19 crisis renewal stage. Viewing a gap in college communities' knowledge regarding COVID‐19 renewal, this study drew upon the extended theoretical model of communal coping and explored college students' stressor coping in back‐to‐campus situations. Results of an online survey (N = 460) across nine US universities, first, supported the presence of communal coping in such a situation. Second, universities' transparent communication benefited students' communal coping appraisal, while online and offline bonding social capital boosted communal coping action; both the appraisal and action components positively contributed to student adaptivity, with life satisfaction being associated only with the action dimension. Theoretical and practical implications for interdisciplinary model concretization and public health crisis renewal are discussed.