Appraising the Quality of Medical Education Research Methods
Bibliographic record
Abstract
PURPOSE: The Medical Education Research Study Quality Instrument (MERSQI) and the Newcastle-Ottawa Scale-Education (NOS-E) were developed to appraise methodological quality in medical education research. The study objective was to evaluate the interrater reliability, normative scores, and between-instrument correlation for these two instruments. METHOD: In 2014, the authors searched PubMed and Google for articles using the MERSQI or NOS-E. They obtained or extracted data for interrater reliability-using the intraclass correlation coefficient (ICC)-and normative scores. They calculated between-scale correlation using Spearman rho. RESULTS: Each instrument contains items concerning sampling, controlling for confounders, and integrity of outcomes. Interrater reliability for overall scores ranged from 0.68 to 0.95. Interrater reliability was "substantial" or better (ICC > 0.60) for nearly all domain-specific items on both instruments. Most instances of low interrater reliability were associated with restriction of range, and raw agreement was usually good. Across 26 studies evaluating published research, the median overall MERSQI score was 11.3 (range 8.9-15.1, of possible 18). Across six studies, the median overall NOS-E score was 3.22 (range 2.08-3.82, of possible 6). Overall MERSQI and NOS-E scores correlated reasonably well (rho 0.49-0.72). CONCLUSIONS: The MERSQI and NOS-E are useful, reliable, complementary tools for appraising methodological quality of medical education research. Interpretation and use of their scores should focus on item-specific codes rather than overall scores. Normative scores should be used for relative rather than absolute judgments because different research questions require different study designs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.675 | 0.860 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.044 | 0.027 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".