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Roland B. Stark
ResearcherPublications, citations & collaboration network
Roland B. Stark is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 30 works, 430 citations, an h-index of 11 and an i10-index of 13.
30
Works
430
Citations
11
h-index
13
i10-index
IDs:OpenAlex
How has Roland B. Stark's publication output changed over time?
ScholarIQpublication output · 2011–2021
Output grew500% over the shown period — from 1 works in 2011 to 6 in 2021.
1
1
3
6
2011201920202021
What are the most-cited papers on Roland B. Stark?
ScholarIQmost cited works
Patient Factors That Matter in Predicting Hip Arthroplasty Outcomes: A Machine-Learning Approach
Jhase Sniderman, Roland B. Stark, Carolyn E. Schwartz, Hajra Imam, Joel Finkelstein, Markku Nousiainen
The Journal of Arthroplasty. 202183 Citations
A new, four‐item instrument to measure workplace bullying
Shellie Simons, Roland B. Stark, Rosanna F. DeMarco
S138039743. 201163 Citations
Capturing patient experience: does quality-of-life appraisal entail a new class of measurement?
Carolyn E. Schwartz, Roland B. Stark, Bruce D. Rapkin
Journal of Patient-Reported Outcomes. 202035 CitationsOPEN ACCESS
Creating idiometric short-form measures of cognitive appraisal: balancing theory and pragmatics
Carolyn E. Schwartz, Roland B. Stark, Bruce D. Rapkin
Journal of Patient-Reported Outcomes. 202132 CitationsOPEN ACCESS
Patient factors that matter in predicting spine surgery outcomes: a machine learning approach
Joel Finkelstein, Roland B. Stark, James Lee, Carolyn E. Schwartz
S4210167578. 202130 Citations
Related on ScholarIQ
DeltaQuest Foundation (United States)
Institution
Patient Factors That Matter in Predicting Hip Arthroplasty Outcomes: A Machine-Learning Approach
Paper
A new, four‐item instrument to measure workplace bullying
Paper
Capturing patient experience: does quality-of-life appraisal entail a new class of measurement?
Paper
Creating idiometric short-form measures of cognitive appraisal: balancing theory and pragmatics
Paper
Patient factors that matter in predicting spine surgery outcomes: a machine learning approach
Paper