Neurology podcast utilization during the COVID-19 pandemic
Bibliographic record
Abstract
Background As medical education shifted to a virtual environment during the early coronavirus disease 2019 (COVID-19) pandemic, we evaluated how neurology podcasting may have been utilized during this period, and which features of podcasts have been more highly sought by a medical audience. Methods We conducted a retrospective analysis of neurology-themed blogs and/or podcasts between April 2019 and May 2020. Programs were eligible if they reported mean monthly downloads > 2000, were affiliated with an academic society, or offered continuing medical education credit. Thirty-day download counts were compared between study months, with adjustment for multiple testing. Exploratory analyses were performed to determine which podcast features were associated with higher downloads. Results Of the 12 neurology podcasts surveyed, 8 completed the survey and 5 met inclusion criteria. The median monthly download count was 2865 (IQR 869–7497), with significant variability between programs (p < 0.001). While there was a 358% increase in downloads during April 2020 when compared to the previous month, this was not significant (median 8124 [IQR 2913–14,177] vs. 2268 [IQR 540–6116], padj = 0.80). The non-significant increase in overall downloads during April 2020 corresponded to an increase in unique episodes during that month (r = 0.48, p = 0.003). There was no difference in 30-day downloads among episodes including COVID-19 content versus not (median 1979 [IQR 791–2873] vs. 1171 [IQR 405–2665], p = 0.28). Conclusions In this unique, exploratory study of academic neurology-themed podcasts, there was no significant increase in episode downloads during the early COVID-19 pandemic. A more comprehensive analysis of general and subspecialty medical podcasts is underway.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".