Development and Initial Validity Evidence for the EvaLeR Tool: Assessing Quality of Emergency Medicine Educational Resources
Bibliographic record
Abstract
ABSTRACT Background Emergency medicine (EM) residents increasingly favor digital educational resources over traditional textbooks, with studies showing over 90% regularly using blogs, podcasts, and other online platforms. No standardized instruments exist to comparatively assess quality across both formats, leading to uncertainty in resource selection and potential inconsistencies in learning. We developed the Evaluation of Learning Resources (EvaLeR) tool and gathered initial validity evidence for its use in assessing both textbooks and digital EM educational resources. Methods This two‐phase mixed‐methods study developed the EvaLeR tool and gathered validity evidence for its use. Phase 1 comprised a systematic literature review, quality indicator analysis, and expert consultation. In Phase 2, 34 EM faculty evaluated 20 resources (10 textbook chapters, 10 blog posts) using EvaLeR. We collected evidence for reliability, internal consistency, and relationships with other variables. Results The EvaLeR tool showed excellent average‐measure reliability (Intraclass correlation coefficient = 0.97, 95% CI [0.94–0.99]). We found high internal consistency (Cronbach's α = 0.86) and moderate correlation with educator gestalt ratings ( r = 0.53, p < 0.001). The tool performed similarly across resource types, with no significant differences between textbook chapters (13.34/18, SD 3.41) and digital resources (13.21/18, SD 3.25; p = 0.62). Conclusions Initial validity evidence supports the use of EvaLeR for quality assessment of both textbooks and digital EM educational resources. This tool provides educators with an evidence‐based approach to resource selection, moving beyond format‐based assumptions to focus on content quality, and represents the first standardized instrument for comparative evaluation across educational resource formats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".