The impact of scholarly podcasts on research distribution and uptake in oncology.
Bibliographic record
Abstract
423 Background: Scholarly podcasts have grown in number and popularity in the post-COVID era. While such podcasts aim to increase research readership and understanding, their impact on these areas has not been quantitatively established. We assessed the relationship between Oncology research podcasts and various research distribution metrics. Methods: All research articles published in the Journal of Clinical Oncology (JCO) from January 2023 to December 2023 were reviewed. Published research articles in the JCO not discussed on the JCO or ASCO Clinical Guidelines podcast were used as controls. Google Scholar citations, Dimensions citations, Mendeley readership, News and Blog shares, Altmetric Attention Score (AAS), social media uptake (i.e., twitter shares), and article downloads were gathered, as well as podcast release dates and the presence of editorial(s) associated with the research article. Mann-Whitney U-tests were used to compare the medians of distribution metrics across podcast and control groups. A multivariate regression analysis incorporating confounding variables (accompanying editorial, subject of research, type of research) was completed. All non-research articles published during the inclusion period were removed from both control and podcast groups. Results: 421 research articles were published during the inclusion period, 57 of which were featured on featured in JCO or ASCO Clinical Guidelines Podcast. The median downloads (p < 0.02), AAS (p < 0.001), and Twitter shares (p <0.001) were significantly higher in the podcast group. Median Twitter shares were 2.3x greater in the podcast group, the largest difference for any metric between podcast and control groups. Median Google Scholar citations, Mendeley readership, News shares, and Dimensions citations were not significantly different between podcast and non-podcast groups. Upon multivariable regression analysis, only Twitter shares were significantly greater in the podcast group (β =22.0; 95% CI: 0.4 – 43.7; p=0.046). Similarly, only Twitter shares were significantly affected by the delay (in days) between podcast release and online publishing of the article (r = -0.284, p = 0.03). Conclusions: Of all distribution metrics, Twitter shares were most positively affected by the association of scholarly podcasts with research published in the JCO, while academic measures (i.e., citations) were unaffected by an association with a podcast. These findings can inform the application of podcasts to increase research uptake amongst target audiences.
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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 | 0.025 | 0.262 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".