Abstract
For evidence-based care, nurses are dependent on latest scientific findings. However, screening and annotating respective publications is a time-consuming process for domain experts. In our ongoing work, we investigate how such experts can be supported by a (semi-)automated, machine-learning based recommendation system and how to design and integrate such a tool into their overall workflow. This paper outlines a current tag recommendation prototype and presents first results from an initial co-design workshop where we investigated the requirements of two nursing care researchers for such a system. The mockups created during the workshops lay the foundation for our goal of integrating a
recommender system seamlessly into the researchers annotation process.
Originalsprache | Englisch |
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Titel | Proceedings of the Workshop on Engaging with Automation co-located with the ACM Conference on Human Factors in Computing Systems (CHI 2022) |
Seiten | 1-5 |
Seitenumfang | 5 |
Publikationsstatus | Veröffentlicht - 2022 |
Veranstaltung | CHI 2022: ACM CHI Conference on Human Factors in Computing Systems - Dauer: 30 Apr. 2022 → 6 Mai 2022 |
Konferenz
Konferenz | CHI 2022: ACM CHI Conference on Human Factors in Computing Systems |
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Zeitraum | 30/04/22 → 6/05/22 |
Research Field
- Ehemaliges Research Field - Capturing Experience
Schlagwörter
- 2. Quartal 2022