Gestalt assessment of online educational resources may not be sufficiently reliable and consistent
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".