Automated Author ProfileSiebenhaar, Katharina U.
Siebenhaar, Katharina U.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.1 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Objective: The COVID-19 pandemic pushed some of the most well-developed healthcaresystems to their limits. In many cases, this has challenged patient-centered care. We set out toexamine individuals’ attitudes towards shared decision making (SDM) and to identifypredictors of participation preference during the pandemic.Methods: We conducted an online survey with a large convenience sample (N = 1061). Ourmain measures of interest were participants’ generic and COVID-19 related participationpreferences, and their acceptance and distress regarding a triage vignette. We also assessedanxiety, e-health literacy, and aspects of participants’ health. We conducted groupcomparisons and multiple linear regression analyses on participation preference and triageacceptance.Results: In generic decision making, most participants expressed a strong need forinformation and a moderate participation preference. In the hypothetical case of COVID-19infection, the majority preferred physician-led decisions. Generic participation preference wasthe strongest predictor for COVID-19 related participation preference, followed by age,education and anxiety. Furthermore, higher generic and COVID-19 related participationpreferences both predicted lower triage acceptance.Conclusion: Our findings demonstrate potential healthcare recipients’ attitudes towards SDMduring a severe healthcare crisis and emphasize that participation preference varies accordingto the context.
Authors
- Köther, Anja K. ;
- Siebenhaar, Katharina U. ;
- Alpers, Georg W.