Medical Education Blog and Podcast Utilization During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction The coronavirus disease 2019 (COVID-19) pandemic disrupted traditional in-person learning models. Free Open Access Medical (FOAM) education resources naturally filled this void, so we evaluated how medical blog and podcast utilization changed during the early months of the pandemic. Methods Academic medical podcast and blog producers were surveyed on blog and podcast utilization immediately before (January-March 2020) and after (April-May 2020) the COVID-19 pandemic declaration and subsequent lockdown. Utilization is quantified in terms of blog post pageviews and podcast downloads. Linear regression was used to estimate the effect of publication during the COVID-19 period on 30-day downloads or pageviews. A linear mixed model was developed to confirm this relationship after adjustment for independent predictors of higher 30-day downloads or pageviews, using the podcast or blog as a random intercept. Results Compared to the pre-pandemic period, downloads and pageviews per unique blog and podcast publication significantly increased for blogs (median 30-day pageviews 802 to 1860, p<0.0001) but not for podcasts (median 30-day downloads 2726 to 1781, p=0.27). Publications that contained COVID-19 content were strongly associated with higher monthly utilization (β=7.21, 95% CI 6.29-8.14 p<0.001), and even non-COVID-19 material had higher utilization in the early pandemic (median 30-day downloads/pageviews 868 to 1380, p<0.0001). Discussion The increased blog pageviews during the early months of the COVID-19 pandemic demonstrated the important role of blogs in rapid knowledge translation. Podcasts did not experience a similar increase in utilization.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".