Clinical judgement in the interpretation of evidence: a Bayesian approach

Harbison, Jean

doi:10.1111/j.1365-2702.2005.01487.x

Cited by 15 publications

(16 citation statements)

References 31 publications

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“…The results of an international survey of nurses' decision-making have supported the broad categories of intuition, quasirationality and analysis (Lauri et al 2001). Cognitive continuum theory facilitates understanding and use of descriptive (peeraided judgement), prescriptive (system-aided judgement) and normative (scientific experiment) decision models (Bell et al 1995), for example, applying Bayes' theorem in nursing (Harbison 2006). Further applications of cognitive continuum theory can be enhanced by addressing critique issues and relating it in more detail to nursing judgement.…”

Section: Pragmaticsmentioning

confidence: 99%

Clinical judgement and decision‐making in nursing – nine modes of practice in a revised cognitive continuum

Standing

2008

Journal of Advanced Nursing

111

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The revised cognitive continuum promotes awareness of the nature and the variety of patient-centred judgement tasks and decisions in nursing, how to select the most suitable intervention tactic from available options, and the fallibility of all forms of human judgement from intuitive/experiential to analytic/rational. Hence, it is recommended for use as an educational tool and practice guide to facilitate theory development and the practice of judgement and decision-making in nursing.

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Section: Pragmaticsmentioning

confidence: 99%

Clinical judgement and decision‐making in nursing – nine modes of practice in a revised cognitive continuum

Standing

2008

Journal of Advanced Nursing

111

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“…Therefore, for example, if CHD is diagnosed less frequently in women, it is because women have lower prevalence of CHD in epidemiologic base rates, and this is assumed to be a straightforward reflection of true biophysiologic differences (Ashby 2006; Harbison 2006). However, a large body of social science research challenges the physiological basis of these differences.…”

mentioning

confidence: 99%

Physician Cognitive Processing as a Source of Diagnostic and Treatment Disparities in Coronary Heart Disease

et al. 2010

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Literature on health disparities documents variations in clinical decision making by patient characteristics, physician attributes, and between healthcare systems. Using data from a vignette-based factorial experiment of 256 primary care providers, we examine the cognitive basis of disparities in the diagnosis and treatment of coronary heart disease (CHD). To determine whether previously observed disparities are due to physicians: (1) not fully considering CHD for certain patients or (2) considering CHD but then discounting it, half of physicians were explicitly directed to consider a CHD diagnosis. Relative to their unprimed counterparts, primed physicians were more likely to order CHD-related tests and prescriptions. However, main effects for patient gender and age remained, suggesting that physicians treated these demographic variables as diagnostic features amounting to lower risk of CHD for these patients. This finding suggests potential for physician appeals to perceived base rates to contribute to the further reification of socially constructed health statistics.

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“…These concepts were behind the development of the Office Assistant in Microsoft Office '97. In the realm of clinical nursing, Bayesian reasoning is being investigated to help with nursing judgment and accepting uncertainty [61]. Also, with respect to transportation and railroad safety, Bayesian methods have been used to measure safety decision benefits while observing uncertainty [62].…”

Section: Bayesian Applications In Other Disciplinesmentioning

confidence: 99%

Bayesian methods for proteomics

et al. 2007

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Biological and medical data have been growing exponentially over the past several years [1,2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad, complex data [4][5][6]. A Bayesian approach can help capture such information and incorporate it seamlessly through a rigorous, probabilistic framework. This paper starts with a review of the background mathematics behind the Bayesian methodology: from parameter estimation to Bayesian networks. The article then goes on to discuss how emerging Bayesian approaches have already been successfully applied to research across proteomics, a field for which Bayesian methods are particularly well suited [7][8][9]. After reviewing the literature on the subject of Bayesian methods in biological contexts, the article discusses some of the recent applications in proteomics and emerging directions in the field.

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Clinical judgement in the interpretation of evidence: a Bayesian approach

Cited by 15 publications

References 31 publications

Clinical judgement and decision‐making in nursing – nine modes of practice in a revised cognitive continuum

Clinical judgement and decision‐making in nursing – nine modes of practice in a revised cognitive continuum

Physician Cognitive Processing as a Source of Diagnostic and Treatment Disparities in Coronary Heart Disease

Bayesian methods for proteomics

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