Workplace-based assessment: effects of rater expertise

Traditional psychometric approaches towards assessment tend to focus exclusively on quantitative properties of assessment outcomes. This may limit more meaningful educational approaches towards workplace-based assessment (WBA). Cognition-based models of WBA argue that assessment outcomes are determined by cognitive processes by raters which are very similar to reasoning, judgment and decision making in professional domains such as medicine. The present study explores cognitive processes that underlie judgment and decision making by raters when observing performance in the clinical workplace. It specifically focuses on how differences in rating experience influence information processing by raters. Verbal protocol analysis was used to investigate how experienced and non-experienced raters select and use observational data to arrive at judgments and decisions about trainees’ performance in the clinical workplace. Differences between experienced and non-experienced raters were assessed with respect to time spent on information analysis and representation of trainee performance; performance scores; and information processing––using qualitative-based quantitative analysis of verbal data. Results showed expert-novice differences in time needed for representation of trainee performance, depending on complexity of the rating task. Experts paid more attention to situation-specific cues in the assessment context and they generated (significantly) more interpretations and fewer literal descriptions of observed behaviors. There were no significant differences in rating scores. Overall, our findings seemed to be consistent with other findings on expertise research, supporting theories underlying cognition-based models of assessment in the clinical workplace. Implications for WBA are discussed

Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis

Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10−4). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10−8), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals