Characterization of an Open-Access Medical News Platform’s Readership During the COVID-19 Pandemic: Retrospective Observational Study
Notice bibliographique
Résumé
BACKGROUND: There are many alternatives to direct journal access, such as podcasts, blogs, and news sites, that allow physicians and the general public to stay up to date with medical literature. However, there is a scarcity of literature that investigates the readership characteristics of open-access medical news sites and how these characteristics may have shifted during the COVID-19 pandemic. OBJECTIVE: This study aimed to assess readership and survey data to characterize open-access medical news readership trends related to the COVID-19 pandemic and overall readership trends regarding pandemic-related information delivery. METHODS: Anonymous, aggregate readership data were obtained from 2 Minute Medicine, an open-access, physician-run medical news organization that has published over 8000 original, physician-written texts and visual summaries of new medical research since 2013. In this retrospective observational study, the average number of article views, number of actions (defined as the sum of the number of views, shares, and outbound link clicks), read times, and bounce rates (probability of leaving a page in <30 s) were compared between COVID-19 articles published from January 1 to May 31, 2020 (n=40) and non-COVID-19 articles (n=145) published in the same time period. A voluntary survey was also sent to subscribed 2 Minute Medicine readers to further characterize readership demographics and preferences, which were scored on a Likert scale. RESULTS: COVID-19 articles had a significantly higher median number of views than non-COVID-19 articles (296 vs 110; U=748.5; P<.001). There were no significant differences in average read times (P=.12) or bounce rates (P=.12). Non-COVID-19 articles had a higher median number of actions than COVID-19 articles (2.9 vs 2.5; U=2070.5; P=.02). On a Likert scale of 1 (strongly disagree) to 5 (strongly agree), our survey data revealed that 65.5% (78/119) of readers agreed or strongly agreed that they preferred staying up to date with emerging literature about COVID-19 by using sources such as 2 Minute Medicine instead of journals. A greater proportion of survey respondents also indicated that open-access news sources were one of their primary sources for staying informed (86/120, 71.7%) compared to the proportion who preferred direct journal article access (61/120, 50.8%). The proportion of readers indicating they were reading one or less full-length medical studies a month were lower following introduction to 2 Minute Medicine compared to prior (21/120, 17.5% vs 38/120, 31.6%; P=.005). CONCLUSIONS: The readership significantly increased for one open-access medical literature platform during the pandemic. This reinforces the idea that open-access, physician-written sources of medical news represent an important alternative to direct journal access for readers who want to stay up to date with medical literature.
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 | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».