The Effects of Expressing Empathy/Autonomy Support Using a COVID-19 Vaccination Chatbot: Experimental Study in a Sample of Belgian Adults.

BACKGROUND: Chatbots are increasingly used to support COVID-19 vaccination programs. Their persuasiveness may depend on the conversation-related context. OBJECTIVE: This study aims to investigate the moderating role of the conversation quality and chatbot expertise cues in the effects of expressing empathy/autonomy support using COVID-19 vaccination chatbots. METHODS: This experiment with 196 Dutch-speaking adults living in Belgium, who engaged in a conversation with a chatbot providing vaccination information, used a 2 (empathy/autonomy support expression: present vs absent) × 2 (chatbot expertise cues: expert endorser vs layperson endorser) between-subject design. Chatbot conversation quality was assessed through actual conversation logs. Perceived user autonomy (PUA), chatbot patronage intention (CPI), and vaccination intention shift (VIS) were measured after the conversation, coded from 1 to 5 (PUA, CPI) and from -5 to 5 (VIS). RESULTS: There was a negative interaction effect of chatbot empathy/autonomy support expression and conversation fallback (CF; the percentage of chatbot answers "I do not understand" in a conversation) on PUA (PROCESS macro, model 1, B=-3.358, SE 1.235, t186=2.718, P=.007). Specifically, empathy/autonomy support expression had a more negative effect on PUA when the CF was higher (conditional effect of empathy/autonomy support expression at the CF level of +1SD: B=-.405, SE 0.158, t186=2.564, P=.011; conditional effects nonsignificant for the mean level: B=-0.103, SE 0.113, t186=0.914, P=.36; conditional effects nonsignificant for the -1SD level: B=0.031, SE=0.123, t186=0.252, P=.80). Moreover, an indirect effect of empathy/autonomy support expression on CPI via PUA was more negative when CF was higher (PROCESS macro, model 7, 5000 bootstrap samples, moderated mediation index=-3.676, BootSE 1.614, 95% CI -6.697 to -0.102; conditional indirect effect at the CF level of +1SD: B=-0.443, BootSE 0.202, 95% CI -0.809 to -0.005; conditional indirect effects nonsignificant for the mean level: B=-0.113, BootSE 0.124, 95% CI -0.346 to 0.137; conditional indirect effects nonsignificant for the -1SD level: B=0.034, BootSE 0.132, 95% CI -0.224 to 0.305). Indirect effects of empathy/autonomy support expression on VIS via PUA were marginally more negative when CF was higher. No effects of chatbot expertise cues were found. CONCLUSIONS: The findings suggest that expressing empathy/autonomy support using a chatbot may harm its evaluation and persuasiveness when the chatbot fails to answer its users' questions. The paper adds to the literature on vaccination chatbots by exploring the conditional effects of chatbot empathy/autonomy support expression. The results will guide policy makers and chatbot developers dealing with vaccination promotion in designing the way chatbots express their empathy and support for user autonomy.

Influence of the COVID-19 pandemic and social media on the behaviour of pregnant and lactating women towards vaccination: a scoping review.

BACKGROUND: Pregnant women, foetuses and infants are at risk of infectious disease-related complications. Maternal vaccination is a strategy developed to better protect pregnant women and their offspring against infectious disease-related morbidity and mortality. Vaccines against influenza, pertussis and recently also COVID-19 are widely recommended for pregnant women. Yet, there is still a significant amount of hesitation towards maternal vaccination policies. Furthermore, contradictory messages circulating social media impact vaccine confidence. OBJECTIVES: This scoping review aims to reveal how COVID-19 and COVID-19 vaccination impacted vaccine confidence in pregnant and lactating women. Additionally, this review studied the role social media plays in creating opinions towards vaccination in these target groups. ELIGIBILITY CRITERIA: Articles published between 23 November 2018 and 18 July 2022 that are linked to the objectives of this review were included. Reviews, articles not focusing on the target group, abstracts, articles describing outcomes of COVID-19 infection/COVID-19 vaccination were excluded. SOURCES OF EVIDENCE: The PubMed database was searched to select articles. Search terms used were linked to pregnancy, lactation, vaccination, vaccine hesitancy, COVID-19 and social media. CHARTING METHODS: Included articles were abstracted and synthesised by one reviewer. Verification was done by a second reviewer. Disagreements were addressed through discussion between reviewers and other researchers. RESULTS: Pregnant and lactating women are generally less likely to accept a COVID-19 vaccine compared with non-pregnant and non-nursing women. The main reason to refuse maternal vaccination is safety concerns. A positive link was detected between COVID-19 vaccine willingness and acceptance of other vaccines during pregnancy. The internet and social media are identified as important information sources for maternal vaccination. DISCUSSION AND CONCLUSION: Vaccine hesitancy in pregnant and lactating women remains an important issue, expressing the need for effective interventions to increase vaccine confidence and coverage. The role social media plays in vaccine uptake remains unclear.

Predicting COVID-19 Symptoms From Free Text in Medical Records Using Artificial Intelligence: Feasibility Study.

BACKGROUND: Electronic medical records have opened opportunities to analyze clinical practice at large scale. Structured registries and coding procedures such as the International Classification of Primary Care further improved these procedures. However, a large part of the information about the state of patient and the doctors' observations is still entered in free text fields. The main function of those fields is to report the doctor's line of thought, to remind oneself and his or her colleagues on follow-up actions, and to be accountable for clinical decisions. These fields contain rich information that can be complementary to that in coded fields, and until now, they have been hardly used for analysis. OBJECTIVE: This study aims to develop a prediction model to convert the free text information on COVID-19-related symptoms from out of hours care electronic medical records into usable symptom-based data that can be analyzed at large scale. METHODS: The design was a feasibility study in which we examined the content of the raw data, steps and methods for modelling, as well as the precision and accuracy of the models. A data prediction model for 27 preidentified COVID-19-relevant symptoms was developed for a data set derived from the database of primary-care out-of-hours consultations in Flanders. A multiclass, multilabel categorization classifier was developed. We tested two approaches, which were (1) a classical machine learning-based text categorization approach, Binary Relevance, and (2) a deep neural network learning approach with BERTje, including a domain-adapted version. Ethical approval was acquired through the Institutional Review Board of the Institute of Tropical Medicine and the ethics committee of the University Hospital of Antwerpen (ref 20/50/693). RESULTS: The sample set comprised 3957 fields. After cleaning, 2313 could be used for the experiments. Of the 2313 fields, 85% (n=1966) were used to train the model, and 15% (n=347) for testing. The normal BERTje model performed the best on the data. It reached a weighted F1 score of 0.70 and an exact match ratio or accuracy score of 0.38, indicating the instances for which the model has identified all correct codes. The other models achieved respectable results as well, ranging from 0.59 to 0.70 weighted F1. The Binary Relevance method performed the best on the data without a frequency threshold. As for the individual codes, the domain-adapted version of BERTje performs better on several of the less common objective codes, while BERTje reaches higher F1 scores for the least common labels especially, and for most other codes in general. CONCLUSIONS: The artificial intelligence model BERTje can reliably predict COVID-19-related information from medical records using text mining from the free text fields generated in primary care settings. This feasibility study invites researchers to examine further possibilities to use primary care routine data.