Derivation of Two Critical Appraisal Scores for Trainees to Evaluate Online Educational Resources: A METRIQ Study
Bibliographic record
Abstract
INTRODUCTION: Online education resources (OERs), like blogs and podcasts, increasingly augment or replace traditional medical education resources such as textbooks and lectures. Trainees' ability to evaluate these resources is poor, and few quality assessment aids have been developed to assist them. This study aimed to derive a quality evaluation instrument for this purpose. METHODS: We used a three-phase methodology. In Phase 1, a previously derived list of 151 OER quality indicators was reduced to 13 items using data from published consensus-building studies (of medical educators, expert podcasters, and expert bloggers) and subsequent evaluation by our team. In Phase 2, these 13 items were converted to seven-point Likert scales used by trainee raters (n=40) to evaluate 39 OERs. The reliability and usability of these 13 rating items was determined using responses from trainee raters, and top items were used to create two OER quality evaluation instruments. In Phase 3, these instruments were compared to an external certification process (the ALiEM AIR certification) and the gestalt evaluation of the same 39 blog posts by 20 faculty educators. RESULTS: Two quality-evaluation instruments were derived with fair inter-rater reliability: the METRIQ-8 Score (Inter class correlation coefficient [ICC]=0.30, p<0.001) and the METRIQ-5 Score (ICC=0.22, p<0.001). Both scores, when calculated using the derivation data, correlated with educator gestalt (Pearson's r=0.35, p=0.03 and r=0.41, p<0.01, respectively) and were related to increased odds of receiving an ALiEM AIR certification (odds ratio=1.28, p=0.03; OR=1.5, p=0.004, respectively). CONCLUSION: Two novel scoring instruments with adequate psychometric properties were derived to assist trainees in evaluating OER quality and correlated favourably with gestalt ratings of online educational resources by faculty educators. Further testing is needed to ensure these instruments are accurate when applied by trainees.
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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 | 0.120 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".