Notice bibliographique
Résumé
In his editorial for the July 2009 issue of Medical Education1 Kevin Eva wrote about witnessing the globalisation of medical education while attending a conference in Pakistan. Just as the conference Kevin and I attended in Pakistan illustrated an exciting development in medical education, I believe this issue of Really Good Stuff is another means of witnessing the globalisation of medical education. Not only is there a great breadth of topics and range of countries represented here, but this issue represents the largest number of Really Good Stuff abstracts we have ever published and that is a testament to the globalisation of medical education as well. We received a record number of submissions for this issue and there were enough ‘really good’ ones to require that more abstracts be published (Thank you, Kevin). There are 17 countries represented by the abstracts published and there were more than 25 countries represented by the submissions for this issue. Really Good Stuff was created as a vehicle to nurture and promote the sharing of new ideas, challenges and solutions to medical education across the continuum and around the world. Because Really Good Stuff represents medical schools throughout the world, it offers a snapshot of the universal nature of the problems faced by medical educators. The abstracts published in this issue provide a glimpse into the ways that schools in India are addressing student well-being; peers are used for tutoring in the Netherlands and for mentoring in Saudi Arabia; and a Chinese medical school where medicine is no longer a foreign language for patients. The importance of promoting an interprofessional, team-based approach to patient care is highlighted in reports from Malawi and Canada and the varied uses of the internet and computer technology are illustrated in reports from Brazil, the US, and the UK. The interest in Really Good Stuff remains high, as demonstrated by the number of submissions received. However, many schools do not submit anything. There are hundreds of medical schools throughout the world. At almost every one of these schools there is activity underway in medical education innovations. Yet these schools are not represented here because they did not submit anything. My hope is that future issues will feature 40 or more different countries. Just as the number of submissions has grown, so have the number of reviewers for Really Good Stuff and the number of different countries they represent and I want to acknowledge the important contributions made by each of the reviewers, both in shaping the contents of this issue and in shaping the direction of Really Good Stuff. Without their thoughtful suggestions and their commitment of time and energy to the review process, there could be no Really Good Stuff. My particular thanks goes to the staff of Medical Education, especially Sue Symons and Amanda Dove, for their unflagging support, guidance, and patience with me throughout the production of Really Good Stuff.
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 | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,235 | 0,132 |
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 ».