BMC Med Inform Decis Mak - The effect of improving task representativeness on capturing nurses' risk assessment judgements: a comparison of written case simulations and physical simulations.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground valid studi describ clinician judgement base respons paper case question common use paper case simul part reflect real clinic environ studi test whether paper case simul evok similar risk assess judgement realist simul patient use high fidel physic simulationsmethod nurs experienc nurs student nurs made dichotom assess risk acut deterior simul scenario paper case physic simul set scenario generat real patient case measur judgement ecolog deriv case record relationship nurs judgement actual patient outcom ie ecolog criteria patient characterist describ use methodolog judgement analysi logist regress model construct calcul len model equat paramet paramet compar model papercas physicalsimul judgementsresult particip signific less achiev ra judg physic simul judg paper case use less model knowledg g physic simul paper case retain similar cognit control consist repeat patient respir rate import cue predict patient risk ecolog model weight heavili participantsconclus extent accuraci judgement analysi studi function task repres improv task repres via high fidel physic simul result lower judgement perform risk assess amongst nurs compar paper case simul len model statist prove use compar differ option design simul use clinic judgement analysi approach outlin may valu design evalu clinic simul part educ train strategi aim improv clinic judgement reason

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