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A systematic review of measurement uncertainty visualizations in the context of standardized assessments.

Aleksander HeltneNiek FransBenjamin HummelenErik FalkumSara Germans SelvikMuirne C S Paap
Published in: Scandinavian journal of psychology (2023)
This systematic review summarized findings of 29 studies evaluating visual presentation formats appropriate for communicating measurement uncertainty associated with standardized clinical assessment instruments. Studies were identified through systematic searches of multiple databases (Medline, Embase, PsycInfo, ERIC, Scopus, and Web of Science). Strikingly, we found no studies which were conducted using samples of clinicians and included clinical decision-making scenarios. Included studies did however find that providing participants with information about measurement uncertainty may increase awareness of uncertainty and promote more optimal decision making. Formats which visualize the shape of the underlying probability distribution were found to promote more accurate probability estimation and appropriate interpretations of the underlying probability distribution shape. However, participants in the included studies did not seem to benefit from the additional information provided by such plots during decision-making tasks. Further explorations into how presentations of measurement uncertainty impact clinical decision making are needed to examine whether findings of the included studies generalize to clinician populations. This review provides an important overview of pitfalls associated with formats commonly used to communicate measurement uncertainty in clinical assessment instruments, and a potential starting point for further explorations into promising alternatives. Finally, our review offers specific recommendations on how remaining research questions might be addressed.
Keyphrases
  • decision making
  • case control
  • systematic review
  • public health
  • health information
  • machine learning
  • mass spectrometry
  • high resolution
  • climate change
  • meta analyses
  • deep learning