Assessing on-line medical education resources: A primer for acute care medical professionals and others
Bibliographic record
Abstract
The internet is increasingly used to propagate medical education, debate, and even disinformation. Therefore, this primer aims to help acute care medical professionals, as well as the public. This is because we all need to be able to critically appraise digital products, appraise content producers, and reflect upon our own on-line presence. This article discusses the challenges and opportunities associated with online medical resources. We then review Free Open Access Medical Education (FOAMed) and the key tools used to assess the trustworthiness of on-line medical products. Specifically, after discussing the pros and cons of traditional academic quality metrics, we compare and contrast the Social Media Index, the ALiEM AIR score, the Revised METRIQ Score, and gestalt. We also discuss internet search engines, peer review, and the important message behind the seemingly tongue-in-cheek Kardashian Index. Hopefully, this primer bolsters basic digital literacy and helps trainees, practitioners, and the public locate useful and reliable on-line resources. Importantly, we highlight the continued importance of traditional academic medicine and primary source publications.
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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 | 0.052 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.018 | 0.045 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".