Quel impact de la présence sur les réseaux sociaux pour un journal médical ? L’exemple de l’équipe dédiée d’Anaesthesia Critical Care and Pain Medicine
Bibliographic record
Abstract
Pour une revue médicale, la diffusion à grande échelle de chacun de ses articles est aujourd’hui indispensable pour toucher un large public scientifique. Dans ce contexte, l’utilisation du réseau social Twitter permet à chaque nouvelle publication d’être diffusée très rapidement, à faible coût, pour des lecteurs précis et partout dans le monde. C’est de loin le réseau social le plus utilisé pour partager les publications en 2021. Les réflexions autour de ce sujet ont ainsi abouti à la création en novembre 2020 de la Social Media Team (SoMe Team) d’Anaesthesia Critical Care and Pain Medicine, le journal anglophone international de la Société française d’anesthésie et de réanimation (SFAR). Cette équipe est coordonnée par un éditeur en charge des réseaux sociaux, en lien direct avec le rédacteur en chef du journal. Il élabore le planning des tweets en fonction du programme de publication du journal, il flèche le type de contenu qui y sera associé et sollicite les auteurs pour l’obtention d’images ou de courtes vidéos de présentation de leur publication. Le contenu graphique et la diffusion sur Twitter sont assurés par les membres de l’équipe dédiée aux réseaux sociaux. For a medical journal, the large-scale dissemination of each of its articles is nowadays essential to reach a large scientific audience. In this context, the use of the social network Twitter allows each new publication to be disseminated very quickly, at low cost, to specific readers and all around the world. It is by far the most used social network for sharing medical publications in 2021. Reflections on this subject have thus led to the creation in November 2020 of the Social Media Team of Anaesthesia Critical Care and Pain Medicine, the international English-language journal of the French Society of Anaesthesia and Intensive Care. This team is coordinated by a Social Media Editor, in direct link with the Editor in Chief of the journal. He establishes the schedule of tweets according to the journal's publication program; he determines the type of content that will be associated with it and solicits the authors to obtain images or short videos presenting their publication. The graphic content and the broadcasting on Twitter are ensured by the team members dedicated to social networks.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".