<scp>FOAM</scp> authorship: Who's teaching our learners?
Bibliographic record
Abstract
Background: Free open-access medical education (FOAM) is extremely popular among learners and educators despite lacking the traditional peer review process. Despite the potential for inaccurate, low-quality, or biased content, little has been published describing FOAM authors. Methods: We performed a cross-sectional analysis of 12 months of content from the top 25 blogs in the 2020 Social Media Index from August 2020-2021. We recorded the number of posts per site and descriptive characteristics of authors, including gender affiliation, conflicts of interest (COI) statements, and type of practice (academic, community, or hybrid). Results: We identified 2141 posts by 1001 authors. More than half were produced by six websites: EM Docs (266), Life in the Fast Lane (232), EMCrit (188), ALiEM (185), Don't Forget the Bubbles (181), and Rebel EM (174). Most content (1680 posts, 78.5%) lacked a COI statement. Authors were mostly academic (89%), mostly held MD degrees (67.4%), and were mostly men (59.7%). Geographically, most FOAM authors reside in the United States (59.5%), Canada (22.42%), or the United Kingdom (9.4%). Conclusions: Of all the posts in the top 25 sites in 2020, more than half came from six sites, and authors were largely North American men in academics with MD degrees. Learners, content creators, and educators should consider the ways in which a more diverse authorship pool might bring value to the FOAM educational experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".