Cost of free open‐access medical education (<scp>FOAM</scp>): An economic analysis of the top 20 <scp>FOAM</scp> sites
Bibliographic record
Abstract
Background: Free open-access medical education (FOAM) is a growing resource within the field of medicine, in particular, emergency medicine. Yet despite FOAM's contribution to advancing medical education, the precise value of FOAM has never been calculated. As a result, content creators have not been acknowledged, either financially or academically, for their deliverables. The aim of this paper was to meet this challenge by determining a value for the top 20 FOAM sites as determined by web traffic in emergency medicine through two approaches. The first approach was to value the websites through a market-based method, where the value of the website was extrapolated from the number of blog posts published. The second approach was through a traffic analysis for each website. Methods: The top 20 FOAM websites in emergency medicine were identified and the monetary value of each resource was calculated through two methods, the first by extrapolating the number of blog posts published by each resource and the second through traffic analysis conducted by a third-party industry specialist based on the number of unique visitors and page visits. Results: The median page views per month was 194,850 and the median number of unique visitors was 138,350. Based on the content valuation method, the median value of content produced in a year was $2337.06 per website. Through the traffic valuation method, the median overall value of a website was $22,815. Conclusions: Although two different approaches were used to value FOAM, both came to the same conclusion that there is substantial economic value being produced. This value should not go unrewarded and content creators should be acknowledged either academically or financially for their contributions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".