P069: Gestalt assessment of online educational resources is unreliable and inconsistent
Bibliographic record
Abstract
Introduction: The use of free open access medicine, particularly open educational resources (OERs), by medical educators and learners continues to increase. As OERs, especially blogs and podcasts, rise in popularity, their ease of dissemination raises concerns about their quality. While critical appraisal of primary research and journal articles is formally taught, no training exists for the assessment of OERs. Thus, the ability of educators and learners to effectively assess the quality of OERs using gestalt alone has been questioned. Our goal is to determine whether gestalt is sufficient for emergency medicine learners (EM) and physicians to consistently rate and reliably recommend OERs to their colleagues. We hypothesized that EM physicians and learners would differ substantively in their assessment of the same resources. Methods: Participants included 31 EM learners and 23 EM attending physicians from Canada and the U.S. A modified Dillman technique was used to administer 4 survey blocks of 10 blog posts per subject between April and August, 2015. Participants were asked whether they would recommend each OER to 1) a learner or 2) an attending physician. The ratings reliability was assessed using single measures intraclass correlations and their correlations amongst the groups were assessed using Spearman’s rho. Family-wise adjustments were made for multiple comparisons using the Bonferroni technique. Results: Learners demonstrated poor reliability when recommending resources for other learners (ICC= 0.21, 95% CI 0.13-0.39) and attending physicians (ICC = 0.16, 95% CI=0.09-0.30). Similarly, attendings had poor reliability when recommending resources for learners (ICC= 0.27, 95% CI 0.18-0.41) and other attendings (ICC=0.22, 95% CI 0.14-0.35). Learners and attendings demonstrated moderate consistency between them when recommending resources for learners (rs=0.494, p<.01) and attendings (rs=0.491, p<.01). Conclusion: Using a gestalt-based rating system is neither reliable nor consistent when recommending OERs to learners and attending physicians. Learners’ gestalt ratings for recommending resources for other learners and attendings were especially unreliable. Our findings suggests the need for structured rating systems to rate OERs.
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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 | 0.023 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".