The geographic and topical landscape of medical education research
Notice bibliographique
Résumé
BACKGROUND: Whether medical education research (MER) is primarily conducted in wealthy countries (in the "Realm of the Rich") is the subject of an ongoing debate. Previous studies of the geography of MER publication output have relied upon proprietary databases, have not compared MER with other fields of study, and have not studied the relationship between authorship geography and topics of study. This study was designed to evaluate the geographic distribution of MER authorship and to relate this to the topics studied in MER. METHODS: Authors' countries of affiliation were identified from PubMed records by parsing and cleaning the text of affiliations and submitting them to the google maps geocoding API. The geography of publication output in MER was compared to other fields using the chi-square goodness-of-fit test. Country income classifications and medical subject heading (MeSH) terms were used to evaluate the topical contributions of countries at different income levels, and simulation was used to compute significance of MeSH term enrichment in MER papers from low income and lower middle income countries. RESULTS: The vast majority of MER papers were contributed by authors based in high income countries. The top four countries were the United States, the United Kingdom, Canada, and Australia, with listed author affiliations in 80% of all MER papers. This percentage was greater in MER than in several other categories, including Biological Science Disciplines (48%), Medicine (69%) and Education (74%), which is a parent category of MER. Authors from low income countries contributed significantly to the topical diversity of MER. MeSH terms associated with government, community health, and health delivery were enriched in papers from low income countries, while terms associated with specialty and clinical training, technology in teaching, and professional obligations (such as workload, burnout, and empathy) were enriched in papers from high income countries. CONCLUSIONS: Geographic disparities in publication output are greater in MER than in any other field examined. The historical origins of MER in North America might explain disproportionate publication output by authors from this region. This study suggests that the MER field benefits from research contributed by authors from low income countries, and also points to potential gaps in MER (and medical education as a whole) in the developing world.
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 | 0,022 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,028 | 0,038 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,013 | 0,008 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».