The Revised METRIQ Score: A Quality Evaluation Tool for Online Educational Resources
Bibliographic record
Abstract
<p>Background: With the rapid proliferation of online medical education resources, quality evaluation is increasingly critical. The Medical Education Translational Resources: Impact and Quality (METRIQ) study evaluated the METRIQ-8 quality assessment instrument for blogs and collected feedback to improve it.</p> <p>Methods: As part of the larger METRIQ study, participants rated the quality of five blog posts on clinical emergency medicine topics using the eight-item METRIQ-8 score. Next, participants used a 7-point Likert scale and free-text comments to evaluate the METRIQ-8 score on ease of use, clarity of items, and likelihood of recommending it to others. Descriptive statistics were calculated and comments were thematically analyzed to guide the development of a revised METRIQ (rMETRIQ) score.</p> <p>Results: A total of 309 emergency medicine attendings, residents, and medical students completed the survey. The majority of participants felt the METRIQ-8 score was easy to use (mean ± SD = 2.7 ± 1.1 out of 7, with 1 indicating strong agreement) and would recommend it to others (2.7 ± 1.3 out of 7, with 1 indicating strong agreement). The thematic analysis suggested clarifying ambiguous questions, shortening the 7-point scale, specifying scoring anchors for the questions, eliminating the "unsure" option, and grouping-related questions. This analysis guided changes that resulted in the rMETRIQ score.</p> <p>Conclusion: Feedback on the METRIQ-8 score contributed to the development of the rMETRIQ score, which has improved clarity and usability. Further validity evidence on the rMETRIQ score is required.</p>
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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 | 0.006 | 0.003 |
| 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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".