Identifying high quality medical education websites in Otolaryngology: A guide for medical students and residents
Bibliographic record
Abstract
BACKGROUND: Learners often utilize online resources to supplement formalized curricula, and to appropriately support learning, these resources should be of high quality. Thus, the objectives of this study are to develop and provide validity evidence supporting an assessment tool designed to assess the quality of educational websites in Otolaryngology- Head & Neck Surgery (ORL-HNS), and identify those that could support effective web-based learning. METHODS: After a literature review, the Modified Education in Otolaryngology Website (MEOW) assessment tool was designed by a panel of experts based on a previously validated website assessment tool. A search strategy using a Google-based search engine was used subsequently to identify websites. Those that were free of charge and in English were included. Websites were coded for whether their content targeted medical students or residents. Using the MEOW assessment tool, two independent raters scored the websites. Inter-rater and intra-rater reliability were evaluated, and scores were compared to recommendations from a content expert. RESULTS: The MEOW assessment tool included a total of 20 items divided in 8 categories related to authorship, frequency of revision, content accuracy, interactivity, visual presentation, navigability, speed and recommended hyperlinks. A total of 43 out of 334 websites identified by the search met inclusion criteria. The scores generated by our tool appeared to differentiate higher quality websites from lower quality ones: websites that the expert "would recommend" scored 38.4 (out of 56; CI [34.4-42.4]) and "would not recommend" 27.0 (CI [23.2-30.9]). Inter-rater and intra-rater intraclass correlation coefficient were greater than 0.7. CONCLUSIONS: Using the MEOW assessment tool, high quality ORL-HNS educational websites were identified.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".