Gestalt assessment of online educational resources may not be sufficiently reliable and consistent Academic Article uri icon

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abstract

  • PURPOSE: Online open educational resources are increasingly used in medical education, particularly blogs and podcasts. However, it is unclear whether these resources can be adequately appraised by end-users. Our goal was to determine whether gestalt-based recommendations are sufficient for emergency medicine trainees and attending physicians to reliably recommend online educational resources to others. METHODS: Raters (33 trainees and 21 attendings in emergency medicine from North America) were asked to rate 40 blog posts according to whether, based on their gestalt, they would recommend the resource to (1) a trainee or (2) an attending physician. The ratings' reliability was assessed using intraclass correlation coefficients (ICC). Associations between groups' mean scores were assessed using Pearson's r. A repeated measures analysis of variance (RM-ANOVA) was completed to determine the effect of the level of training on gestalt recommendation scale (i. e. trainee vs. attending). RESULTS: Trainees demonstrated poor reliability when recommending resources for other trainees (ICC = 0.21, 95% CI 0.13-0.39) and attendings (ICC = 0.16, 95% CI = 0.09-0.30). Similarly, attendings had poor reliability when recommending resources for trainees (ICC = 0.27, 95% CI 0.18-0.41) and other attendings (ICC = 0.22, 95% CI 0.14-0.35). There were moderate correlations between the mean scores for each blog post when either trainees or attendings considered the same target audience. The RM-ANOVA also corroborated that there is a main effect of the proposed target audience on the ratings by both trainees and attendings. CONCLUSIONS: A gestalt-based rating system is not sufficiently reliable when recommending online educational resources to trainees and attendings. Trainees' gestalt ratings for recommending resources for both groups were especially unreliable. Our findings suggest the need for structured rating systems to rate online educational resources.

authors

  • Krishnan, Keeth
  • Thoma, Brent
  • Trueger, N Seth
  • Lin, Michelle
  • Chan, Teresa

publication date

  • April 2017