Quality Evaluation Scores are no more Reliable than Gestalt in Evaluating the Quality of Emergency Medicine Blogs: A METRIQ Study
Bibliographic record
Abstract
Construct: We investigated the quality of emergency medicine (EM) blogs as educational resources. PURPOSE: Online medical education resources such as blogs are increasingly used by EM trainees and clinicians. However, quality evaluations of these resources using gestalt are unreliable. We investigated the reliability of two previously derived quality evaluation instruments for blogs. APPROACH: Sixty English-language EM websites that published clinically oriented blog posts between January 1 and February 24, 2016, were identified. A random number generator selected 10 websites, and the 2 most recent clinically oriented blog posts from each site were evaluated using gestalt, the Academic Life in Emergency Medicine (ALiEM) Approved Instructional Resources (AIR) score, and the Medical Education Translational Resources: Impact and Quality (METRIQ-8) score, by a sample of medical students, EM residents, and EM attendings. Each rater evaluated all 20 blog posts with gestalt and 15 of the 20 blog posts with the ALiEM AIR and METRIQ-8 scores. Pearson's correlations were calculated between the average scores for each metric. Single-measure intraclass correlation coefficients (ICCs) evaluated the reliability of each instrument. RESULTS: Our study included 121 medical students, 88 EM residents, and 100 EM attendings who completed ratings. The average gestalt rating of each blog post correlated strongly with the average scores for ALiEM AIR (r = .94) and METRIQ-8 (r = .91). Single-measure ICCs were fair for gestalt (0.37, IQR 0.25-0.56), ALiEM AIR (0.41, IQR 0.29-0.60) and METRIQ-8 (0.40, IQR 0.28-0.59). CONCLUSION: The average scores of each blog post correlated strongly with gestalt ratings. However, neither ALiEM AIR nor METRIQ-8 showed higher reliability than gestalt. Improved reliability may be possible through rater training and instrument refinement.
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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 | 0.034 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".