A Blog Literacy Level Project: Analyzing the Relationship Between FOAMed Resource Characteristics in Blog Posts and Knowledge Dissemination
Bibliographic record
Abstract
Introduction: Growing evidence supports the use of social media in medical education. One benefit is individual customization of the learning environment. Few investigators, however, have examined the stylistic characteristics of Free Open Access Medical Education (FOAMed) resources. Objective: We investigated the ideal reading level of FOAMed blog posts. Methods: We collected posts from the BoringEM.org blog, a multiauthor, peer-reviewed emergency medicine FOAMed blog. Posts are created by students, residents, and physicians; the resulting text varies in literary ease. We used Wordpress Page to extract the Flesch Reading Ease Score (FRES), a measure of literary difficulty. We used Google Analytics to track page views, unique page views, and cities reached, as markers of dissemination. Results: We included 6 months of blog posts (58 articles) in our final analysis. Pearson correlation showed no association between FRES and number of page views (r 1⁄4 0.138, P 1⁄4 .31), unique page views (r 1⁄4 0.143, P 1⁄4 .29), or number of cities reached (r 1⁄4 0.002, P 1⁄4 .99). There was a moderate correlation between word count and number of page views (r 1⁄4 0.38, P , .001) and word count and cities reached (r 1⁄4 0.36, P , .001). Conclusions: We were not able to identify an optimal reading level for FOAMed posts, as FRES were not correlated with markers of dissemination. This may be due to the high reading level of medical practitioners. Ultimately, subgroup analyses examining the characteristics of our readership may shed light on the literary characteristics that appeal to these groups.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".