Twitter Users with Access to Academic Library Services Request Health Sciences Literature through Social Media
Notice bibliographique
Résumé
A Review of:
 Swab, M., & Romme, K. (2016). Scholarly sharing via Twitter: #icanhazpdf requests for health sciences literature. Journal of the Canadian Health Libraries Association, 37(1), 6-11. http://dx.doi.org/10.5596/c16-009
 
 Abstract
 
 Objective – To analyze article sharing requests for health sciences literature on Twitter, received through the #icanhazpdf protocol.
 
 Design – Social media content analysis.
 
 Setting – Twitter.
 
 Subjects – 302 tweets requesting health sciences articles with the #icanhazpdf tag.
 
 Methods – The authors used a subscription service called RowFeeder to collect public tweets posted with the hashtag #icanhazpdf between February and April 2015. Rowfeeder recorded the Twitter user name, location, date and time, URL, and content of the tweet. The authors excluded all retweets and then each reviewed one of two sets. They recorded the geographic region and affiliation of the requestor, whether the tweet was a request or comment, type of material requested, how the item was identified, and if the subject of the request was health or non-health. Health requests were further classified using the Scopus subject category of the journal. A journal could be classified with more than one category. Any uncertainties during the coding process were resolved by both authors reviewing the tweet and reaching a consensus.
 
 Main results – After excluding all the retweets and comments, 1079 tweets were coded as heath or non-health related. A final set of 302 health related requests were further analyzed. Almost all the requests were for journal articles (99%, n=300). The highest-ranking subject was medicine (64.9%, n=196), and the lowest was dentistry (0.3%, n=1). The most common article identifier was a link to the publisher’s website (50%, n=152), followed by a link to the PubMed record (22%, n=67). Articles were also identified by citation information (11%, n=32), DOI (5%, n=14), a direct request to an individual (3%, n=9), another method (2%, n=6), or multiple identifiers (7%, n=22). The majority of requests originated from the UK and Ireland (29.1%, n=88), the United States (26.5%, n=80), and the rest of Europe (19.2%, n=58. Many requests came from people with affiliations to an academic institution (45%, n=136). These included librarians (3.3%, n=10), students (13.6%, n=41), and academics (28.1%, n=85). When tweets of unknown affiliation were excluded (n=117), over 70% of the requests were from people with academic links. Other requesters included journalists, clinicians, non-profit organisations, patients, and industry employees. The authors examined comments in the tweets to gain some understanding of the reasons for seeking articles through #icanhazpdf, although this was not the primary focus of their study. A preliminary examination of the comments suggested that users value the ease, convenience, and the ability to connect with other researchers that social media offers.
 
 Conclusion – The authors concluded that the number of requests for health sciences literature through this channel is modest, but health librarians should be aware of #icanhazpdf as another method through which their users might seek to obtain articles. The authors recommend further research into the reasons why users sometimes choose social media over the library to obtain articles.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,005 | 0,508 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».