FOAM authorship: Who’s teaching the learners?
Bibliographic record
Abstract
Learning Objectives: Of all posts from the top 25 blogs in 2020, more than half came from six sites, most contained clinical content, and authors were largely North American male academics with MD degrees. Learners, content-creators, and educators must recognize these limitations in utilizing online educational content.Background: While use of Free Open Access Medical Education (FOAM) content has grown over the last decade, concerns about quality assessment remain. Given the disconnect between the high utilization of these resources by learners and the low barriers and oversight to publishing, the authors of FOAM resources require further scrutiny.Objectives: We sought to describe the production and authorship characteristics of the most impactful FOAM blogs.Methods: Based on previous studies, a classification system for post content was developed by to two authors with content expertise in online educational resources. We included 12 months (August, 2019 - May, 2021) of blog posts from each of the top 25 sites in the 2020 social media index (SMI). We recorded the following: number of posts per site and per author, types of post; and author related details such as gender, title, affiliation, degree, location of practice and type of practice (academic, community, or hybrid). Gender was determined based on an online identification tool (genderchecker.com).Results: We identified 2,141 posts by 1,001 authors, with more than half 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 conflicts of interest (COI) statement. Posts averaged 5.9 + 11.1 references and 2.32 + 7.8 comments. Authors were mostly academic (89%), mostly held MD degrees (67.4%), and skewed male (59.7%). Geographically, most FOAM authors reside in the USA (59.5%), Canada (22.42%), or the UK (9.4%).Conclusions: Of all the posts in the top 25 blogs in 2020, more than half came from six sites, most contained clinical content, and authors were largely North American male 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.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".