Determining Impact for Anatomical Sciences Education Articles in the Age of Altmetrics
Notice bibliographique
Résumé
The impact of academic scholarship has been traditionally measured using citation‐based metrics. In recent years, new platforms (such as social media tools like Twitter and Facebook, and bibliography tools like Mendeley) have become available to allow for dissemination of scholarly papers to both academic and non‐academic audiences. Alternative or article‐level metrics (altmetrics) related to paper's visibility on these platforms, as well as the Altmetric Score (ALT Score, a global index representing attention generated across all platforms tracked by Altmetric.com), may therefore be useful in characterizing scholarly impact. However, the relationship between traditional measures of impact and altmetrics is unclear for papers in the anatomical sciences education field. The purpose of this study was to quantitatively assess relationships between specific altmetrics, global ALT Scores, and citations for articles in the journal Anatomical Sciences Education (ASE). In addition, qualitative characteristics of highly cited papers (impactful by traditional measures) and papers with high ALT Scores were determined. A database study was performed in November 2017 utilizing the Altmetric Explorer tool (Altmetric.com) for ASE articles published between 2012 and 2017, yielding 340 articles. Citation counts for these papers were collected from Scopus. Correlation coefficients between variables were then determined. Abstracts were textually analyzed and coded to qualitatively identify emerging themes in the top 100 highly cited papers and the papers with the top 100 ALT Scores. The only platforms that featured non‐zero data for the majority (>50%) of articles were Twitter and Mendeley. Most platforms tracked by Altmetric.com were rarely (<20%) used for disseminating ASE articles. ALT Scores were most strongly correlated with Twitter mentions, and featured relatively weak correlations with mentions on Facebook, news articles, or blog posts, as well as citation counts. The only altmetric that strongly correlated with citations was Mendeley download counts. The most dominant theme in papers with both high ALT Scores and high citation counts was the application of modern technologies (ultrasound, 3D printing, and Youtube) in anatomy teaching. Other themes (ex. student‐centered learning, body donation, and modern curriculum development) were present in highly cited papers but were not as pervasive when looking at papers with strong ALT Scores. In conclusion, caution is necessary in terms of interpreting the ALT Score as a global index of attention, since the ALT Score was almost entirely driven by one platform (Twitter). However, specific altmetrics do hold potential in characterizing impact that may reinforce (Mendeley) or complement (Twitter) scholarly impact traditionally depicted by citation counts for papers in the anatomical sciences education field. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,017 | 0,180 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,036 | 0,042 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,007 |
| 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 | 0,002 |
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 ».