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What is an expert? A systems perspective on expertise.

ABSTRACT: Expert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) was chosen for this purpose. For illustration, we focused on taxonomic expertise. The model structure was developed in consultation with taxonomists. The relative importance of the factors within the network was determined by a second set of taxonomists (supra-experts) who also provided validation of the model structure. Model performance was assessed by applying the model to hypothetical career states of taxonomists designed to incorporate known differences in career states for model testing. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher-order nodes, "Quality of work" and "Total productivity", had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite the differences in the node weights provided by our supra-experts, there was good agreement among assessments of our hypothetical experts that accurately reflected differences we had specified. This systems approach provides a way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

SUBMITTER: Caley MJ

PROVIDER: S-EPMC3925425 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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What is an expert? A systems perspective on expertise.

Caley Michael Julian MJ O'Leary Rebecca A RA Fisher Rebecca R Low-Choy Samantha S Johnson Sandra S Mengersen Kerrie K

Ecology and evolution 20131226 3

Expert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) ...[more]

PMID: 24558579

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Project description:BackgroundPerception of stimuli presented in a virtual dentistry environment affects regions of the brain that are related to pain perception.ObjectiveWe investigated whether neural correlates of virtual pain perception are affected by education in dentistry.MethodsIn this functional magnetic resonance imaging study, a sample of 20 dental students and 20 age-matched controls viewed and listened to video clips presenting a dental treatment from the first-person perspective. An anxiety questionnaire was used to assess the level of dental anxiety. Neural correlates of pain perception were investigated through classic general linear model analysis and in-house classification methods.ResultsDental students and naïve controls exhibited similar anxiety levels for invasive stimuli. Invasive dentistry scenes evoked a less affective component of pain in dental students compared with naïve controls (P<.001). Reduced affective pain perception went along with suppressed brain activity in pain matrix regions including the insula, anterior cingulate cortex, and basal ganglia. Furthermore, a substantial reduction of brain activity was observed in motor-related regions, particularly the supplementary motor area, premotor cortex, and basal ganglia. Within this context, a classifier analysis based on neural activity in the nucleus lentiformis could identify dental students and controls on the individual subject level in 85% of the cases (34 out of 40 participants, sensitivity=90%, specificity=80%).ConclusionsVirtual dental treatment activates pain-related brain regions in controls. By contrast, dental students suppress affective and motor-related aspects of pain. We speculate that dental students learn to control motoric aspects of pain perception during their education because it is a prerequisite for the professional manual treatment of patients. We discuss that a specific set of learning mechanisms might affect perceived self-efficacy of dental students, which in turn might reduce their affective component of pain perception.

Project description:BACKGROUND:Increasing numbers of patients consult Web-based rating platforms before making health care decisions. These platforms often provide ratings from other patients, reflecting their subjective experience. However, patients often lack the knowledge to be able to judge the objective quality of health services. To account for this potential bias, many rating platforms complement patient ratings with more objective expert ratings, which can lead to conflicting signals as these different types of evaluations are not always aligned. OBJECTIVE:This study aimed to fill the gap on how consumers combine information from 2 different sources-patients or experts-to form opinions and make purchase decisions in a health care context. More specifically, we assessed prospective patients' decision making when considering both types of ratings simultaneously on a Web-based rating platform. In addition, we examined how the influence of patient and expert ratings is conditional upon rating volume (ie, the number of patient opinions). METHODS:In a field study, we analyzed a dataset from a Web-based physician rating platform containing clickstream data for more than 5000 US doctors. We complemented this with an experimental lab study consisting of a sample of 112 students from a Dutch university. The average age was 23.1 years, and 60.7% (68/112) of the respondents were female. RESULTS:The field data illustrated the moderating effect of rating volume. If the patient advice was based on small numbers, prospective patients tended to base their selection of a physician on expert rather than patient advice (profile clicks beta=.14, P<.001; call clicks beta=.28, P=.03). However, when the group of patients substantially grew in size, prospective patients started to rely on patients rather than the expert (profile clicks beta=.23, SE=0.07, P=.004; call clicks beta=.43, SE=0.32, P=.10). The experimental study replicated and validated these findings for conflicting patient versus expert advice in a controlled setting. When patient ratings were aggregated from a high number of opinions, prospective patients' evaluations were affected more strongly by patient than expert advice (meanpatient positive/expert negative=3.06, SD=0.94; meanexpert positive/patient negative=2.55, SD=0.89; F1,108=4.93, P=.03). Conversely, when patient ratings were aggregated from a low volume, participants were affected more strongly by expert compared with patient advice (meanpatient positive/expert negative=2.36, SD=0.76; meanexpert positive/patient negative=3.01, SD=0.81; F1,108=8.42, P=.004). This effect occurred despite the fact that they considered the patients to be less knowledgeable than experts. CONCLUSIONS:When confronted with information from both sources simultaneously, prospective patients are influenced more strongly by other patients. This effect reverses when the patient rating has been aggregated from a (very) small number of individual opinions. This has important implications for how to present health care provider ratings to prospective patients to aid their decision-making process.

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What is an expert? A systems perspective on expertise.

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What is an expert? A systems perspective on expertise.

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