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1.  The effect of improving task representativeness on capturing nurses’ risk assessment judgements: a comparison of written case simulations and physical simulations 
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.
doi:10.1186/1472-6947-13-62
PMCID: PMC3674950  PMID: 23718556
Written case simulation; Physical simulation; Representative design; Clinical judgement analysis; Risk assessment; Lens model equation; Logistic regression; Clinical vignettes
2.  Interpreting the psychometric properties of the components of primary care instrument in an elderly population 
Objective:
To determine the psychometric properties of the Components of Primary Care Instrument (CPCI) in a patient population aged 65 or older.
Materials and Methods:
795 participants in the OKLAHOMA Studies, a longitudinal population-based study of predominantly Caucasian, elderly patients, completed the CPCI. Reliability analysis and confirmatory factor analysis were done to provide psychometric properties for this elderly sample. Models were constructed and tested to determine the best fit for the data including the addition of a method factor for negatively worded items.
Results:
Cronbach's alphas were comparable to values reported in prior studies. The confirmatory factor analysis with factor inter-correlations and a method factor each improved the fit of the factor model to the data. The combined model's fit approached the level conventionally recognized as adequate.
Conclusion:
CPCI appears to be a reliable tool for describing patient perceptions of the quality of primary care for patients over age 65.
doi:10.4103/2230-8229.98299
PMCID: PMC3410175  PMID: 22870416
Components of primary care instrument; elderly; older patients; primary care; reliability; validity

Results 1-2 (2)