Evaluating the reliability of gestalt quality ratings of medical education podcasts: A METRIQ study
Bibliographic record
Abstract
INTRODUCTION: Podcasts are increasingly being used for medical education. Studies have found that the assessment of the quality of online resources can be challenging. We sought to determine the reliability of gestalt quality assessment of education podcasts in emergency medicine. METHODS: An international, interprofessional sample of raters was recruited through social media, direct contact, and the extended personal network of the study team. Each participant listened to eight podcasts (selected to include a variety of accents, number of speakers, and topics) and rated the quality of that podcast on a seven-point Likert scale. Phi coefficients were calculated within each group and overall. Decision studies were conducted using a phi of 0.8. RESULTS: A total of 240 collaborators completed all eight surveys and were included in the analysis. Attendings, medical students, and physician assistants had the lowest individual-level variance and thus the lowest number of required raters to reliably evaluate quality (phi >0.80). Overall, 20 raters were required to reliably evaluate the quality of emergency medicine podcasts. DISCUSSION: Gestalt ratings of quality from approximately 20 health professionals are required to reliably assess the quality of a podcast. This finding should inform future work focused on developing and validating tools to support the evaluation of quality in these resources.
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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 | 0.062 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".