Digital Impact Factor: A Quality Index for Educational Blogs and Podcasts in Emergency Medicine and Critical Care
Bibliographic record
Abstract
STUDY OBJECTIVE: Given the popularity of educational blogs and podcasts in medicine, learners and educators need tools to identify trusted and impactful sites. The Social Media Index was a multi-sourced formula to rank the effect of emergency medicine and critical care blogs. In 2022, a key data point for the Social Media Index became unavailable. This bibliometric study aimed to develop a new measure, the Digital Impact Factor, as a replacement. METHODS: The Digital Impact Factor incorporated modern measures of website authority and reach. This formula was applied to a cross-sectional study of active emergency medicine and critical care blogs and podcasts. For each website, we generated a Digital Impact Factor score based on Ahrefs Domain Rating and the follower count of the websites' pages from 8 social media platforms. A series of Spearman correlations provided evidence of association by comparing a rank-ordered list to rank lists derived from the Social Media Index over the last 5 years. The Bland-Altman analysis assessed for agreement. RESULTS: The authors identified 88 relevant websites with a median Ahrefs Domain Rating of 28 (range 0 to 71, maximum 100) and total social media followership count across 8 platforms of 1,828,557. The Domain Rating and individual social media followership scores were normalized based on the highest recorded values to yield the Digital Impact Factor (median 4.57; range 0.02 to 9.50, maximum 10). The correlation between the 2022 Digital Impact Factor and the 2021 Social Media Index was 0.94 (95% confidence interval 0.89 to 0.97; p<.001; n=41 rankings correlated), suggesting that they measure similar constructs. The Bland-Altman plot also demonstrated fair agreement between the 2 scores. CONCLUSION: The Digital Impact Factor is a measure of the relative effect of educational blogs and podcasts within emergency medicine and critical care.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".