To determine the long-term impacts of attending the Un-Meeting.
On Saturday, June 1st, 2019 an Un-Meeting addressing the topics of Machine Learning and Artificial Intelligence Applications in Translational Science was held at the University of Rochester. An evaluation survey was sent to all meeting participants following the Un-Meeting. The survey consisted of both quantitative and qualitative items pertaining to meeting content/logistics, meeting experience and the meeting overall. Of the 93 meeting attendees, 60 responses were cultivated for a response rate of 64.5%. Demographically, attendees reflected a multitude of disciplines but most were from academic institutions and/or CTSA Program hubs.
With regard to meeting content, most respondents agree that the Un-Meeting covered valuable topics, addressed participants’ expectations, and allotted an adequate amount of networking time. Most participants also suggest that they were satisfied, or very satisfied, with meeting logistics such as the registration process, location, facilities and hotel accommodations. Overall, the Un-Meeting achieved an average rating of 8.54, and a median of 8.5.
Data collected via the evaluation survey also reveal that over 90.0% of participants report making new connections and/or learned something new as a result of the Un-Meeting. In addition, 50 respondents suggest that they intend to pursue actions such collaborating on a publication/grant proposal, implement a new research idea, develop a pilot project/program, organize a follow-up meeting, etc.
Most qualitative data collected by the instrument were categorized by overarching themes. Some qualitative comments were split, as the data fit within more than one theme. Several example quotes for each qualitative question were organized into tables with the corresponding question and theme in the report. All quotes not represented by theme can be found in the appendix.
Qualitative data reveal that meeting participants connected with potential collaborators and other experts in the field at the Un-Meeting. Respondents identified 25 unique partners with whom they may collaborate as a result of the Un-Meeting. Data also suggest that many participants gained general content knowledge, common problems/limitations and new tools/novel methods of Machine Learning and Artificial Intelligence Applications in translational science as a result of the Un-Meeting. Respondents also emphasize that both networking and problem sharing were a large benefit of the Un-Meeting.
In terms of meeting feedback, survey respondents indicate that the time to network, overall meeting format and breakout/discussion sessions were particularly helpful. In contrast, suggestions for meeting improvements included themes such as timing/duration, better meeting preparation and the request for actionable items after the Un-Meeting. Overall, the Un-Meeting was well received by participants.
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