The impact of scholarly podcasts on research distribution and uptake in oncology.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,262 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,009 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».