Notice bibliographique
Résumé
In Reply to Farnan et al and to Lemon: We thank Farnan, Landon, and Arora for their observations on the restrictions to using social media in many hospitals; we echo their concerns that this obstructs health professionals from learning and augmenting their practices using these technologies. This relates to a broader problem that technologies used in hospitals, such as those used in e-health, are also not well aligned with learning.1 Clearly, there is a growing divergence between the learning needs of health care professionals and the pursuit of hospital IT governance. Lemon rightly raises concerns over the monetization of social media channels, something that has grown quite significantly since we posted the videos discussed in our article, and even since we submitted the manuscript of that article. At one level this may simply reflect the adage that “there’s no such thing as a free lunch.” The growing concern over the influence of commercial interactions with medical education, reflected in the development of institutional and journal conflict-of-interest policies, indicates that we need to consider that influence more directly. A simplistic perspective offers two alternatives: open but commercially tinged social media with a vast but anonymous audience, or closed and academically “clean” media targeted at a known and relatively small audience. However, given that many academic publishers are now adding advertising to their journals’ Web sites (even if it is usually for their own commercial services), these alternatives would be end points of a continuum. We agree with Lemon’s suggestion that we need “broad academic guidelines for those wishing to publish academic and education material for use on social platforms.” Finally, we share Lemon’s concerns that what constitutes an academic publication has not kept pace with the proliferation of channels and forms of publishing now available to medical educators. Principles of scrutiny and accountability are not lost in social media, but they are profoundly altered, particularly as the voice of the individual peer expert is exchanged for the many and varied voices of the crowd. Although our standards may not change, they are likely to be expressed and challenged in new and unexpected ways by social media. We see the debate on these issues as an indication of the health of scholarship in medical education rather than a portent of its imminent demise. Rachel Ellaway, PhD Assistant dean for undergraduate medical education, Northern Ontario School of Medicine, Sudbury, Ontario, Canada. David Topps, MD Professor of family medicine, University of Calgary, Alberta, Canada; [email protected] Joyce Helmer, EdD Associate professor, Division of Human Sciences, Northern Ontario School of Medicine, Sudbury, Ontario, Canada.
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 | 0,006 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,031 | 0,034 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,073 | 0,056 |
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 ».