The Revised METRIQ Score: A Quality Evaluation Tool for Online Educational Resources
Bibliographic record
Abstract
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. 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. 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. 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.
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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 | 0.031 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".