title: The effect of improving task representativeness on capturing nurses' risk assessment judgements: a comparison of written case simulations and physical simulations.
abstract: BACKGROUND
  The validity of studies describing clinicians' judgements based on their responses to paper cases is questionable, because - commonly used - paper case simulations only partly reflect real clinical environments. In this study we test whether paper case simulations evoke similar risk assessment judgements to the more realistic simulated patients used in high fidelity physical simulations.
  
  
  METHODS
  97 nurses (34 experienced nurses and 63 student nurses) made dichotomous assessments of risk of acute deterioration on the same 25 simulated scenarios in both paper case and physical simulation settings. Scenarios were generated from real patient cases. Measures of judgement 'ecology' were derived from the same case records. The relationship between nurses' judgements, actual patient outcomes (i.e. ecological criteria), and patient characteristics were described using the methodology of judgement analysis. Logistic regression models were constructed to calculate Lens Model Equation parameters. Parameters were then compared between the modeled paper-case and physical-simulation judgements.
  
  
  RESULTS
  Participants had significantly less achievement (ra) judging physical simulations than when judging paper cases. They used less modelable knowledge (G) with physical simulations than with paper cases, while retaining similar cognitive control and consistency on repeated patients. Respiration rate, the most important cue for predicting patient risk in the ecological model, was weighted most heavily by participants.
  
  
  CONCLUSIONS
  To the extent that accuracy in judgement analysis studies is a function of task representativeness, improving task representativeness via high fidelity physical simulations resulted in lower judgement performance in risk assessments amongst nurses when compared to paper case simulations. Lens Model statistics could prove useful when comparing different options for the design of simulations used in clinical judgement analysis. The approach outlined may be of value to those designing and evaluating clinical simulations as part of education and training strategies aimed at improving clinical judgement and reasoning.
authors.affiliation: Centre for Reviews and Dissemination, University of York, York YO10 5DD, UK. huiqin.yang@york.ac.uk
authors.forename: Huiqin
authors.initials: H
authors.lastname: Yang
authors.forename: Carl
authors.initials: C
authors.lastname: Thompson
authors.forename: Robert M
authors.initials: RM
authors.lastname: Hamm
authors.forename: Martin
authors.initials: M
authors.lastname: Bland
authors.forename: Alison
authors.initials: A
authors.lastname: Foster
mesh_terms: D002983:Clinical Competence; D004506:Education, Nursing / Q000379:methods*; D004521:Educational Measurement / Q000379:methods*; D006801:Humans; D007600:Judgment; D016294:Models, Nursing; D016544:Patient Simulation; D018570:Risk Assessment