Locating Medical Information during an Infodemic: Information Seeking Behavior and Strategies of Health-Care Workers in Germany

BACKGROUND

The COVID-19 pandemic has led to a flood of-often contradictory-evidence. HCWs had to develop strategies to locate information that supported their work. We investigated the information-seeking of different HCW groups in Germany.

METHODS

In December 2020, we conducted online surveys on COVID-19 information sources, strategies, assigned trustworthiness, and barriers-and in February 2021, on COVID-19 vaccination information sources. Results were analyzed descriptively; group comparisons were performed using χ²-tests.

RESULTS

For general COVID-19-related medical information (413 participants), non-physicians most often selected official websites (57%), TV (57%), and e-mail/newsletters (46%) as preferred information sources-physicians chose official websites (63%), e-mail/newsletters (56%), and professional journals (55%). Non-physician HCWs used Facebook/YouTube more frequently. The main barriers were insufficient time and access issues. Non-physicians chose abstracts (66%), videos (45%), and webinars (40%) as preferred information strategy; physicians: overviews with algorithms (66%), abstracts (62%), webinars (48%). Information seeking on COVID-19 vaccination (2700 participants) was quite similar, however, with newspapers being more often used by non-physicians (63%) vs. physician HCWs (70%).

CONCLUSION

Non-physician HCWs more often consulted public information sources. Employers/institutions should ensure the supply of professional, targeted COVID-19 information for different HCW groups.

Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification

User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the "July 20 heavy rainstorm in Zhengzhou" posted on China's largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users' attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.

GoCC4All a pervasive technology to provide access to TV to the deafblind community

Considering the importance of communication and independence for the deafblind community, this work presents findings of the use of technology to address the lack of information due to communication challenges among the deafblind community. Over time, many investigations have been carried out regarding this matter, but very few providing solution, which is why this study emerged, looking to making all the information broadcasted through television accessible for this community. The work team designed a technology (GoCC4All) to address the needs of the deafblind community. GoCC4All provides access to captions available on TV through braille displays and mobile devices. Our research process and results outline the path for creating, adapting, and adopting new technologies for people with disabilities who have the right to access the information just as their peers without disabilities. The information in this paper is based on two surveys, an initial beta testing (BT) and a final survey among a group of 14 users (UT) who tested the GoCC4All application. Our findings support the positive impact of the iterative creation of assistive technology based on users' experience and users' recommendations to better serve the needs of the deafblind community.