In Reply to Fyfe and Douglass
Notice bibliographique
Résumé
We thank Drs. Fyfe and Douglass for their comments on our Perspective. The authors have pinpointed an important issue that has plagued medical school directors as well as anyone interested in the physician shortages in underserved settings, particularly in rural locations. Certainly, the finding that dissatisfaction (due to stress and family considerations) increases the likelihood of leaving rural practices is an issue of concern.1,2 However, we would argue that we still need to recruit not only from but also for setting. As there has been a documented inadequacy in the number of rural and culturally diverse applicants to many medical schools,3 we simply do not have sufficient numbers to determine whether those physicians who are unhappy with their rural placement were actually recruited from that area to be for that area. To try to unravel this problem of achieving a truly diverse and equitable workforce with a high degree of job satisfaction, we need to understand the distinction between the physicians from a privileged background of higher socioeconomic status and urbanism who were incentivized to practice in an underserved setting and those who were successful in coming from the targeted setting and are now working for that setting. What are their career expectations and satisfaction? How much influence did the urban medical schools have in influencing those expectations? There is good evidence that rural-born physicians are more than 4 times as likely to practice in rural areas and physicians who train at rural medical schools or who have rural training experiences are more likely to practice in rural settings.4,5 As we move forward to address this key doctor shortage and training research agenda, these will be priority questions. We thank Drs. Fyfe and Douglass for bringing a crucial nuance to this fundamental problem. Melanie Raffoul, MDAttending physician, assistant professor, Department of Emergency Medicine, and assistant medical director, Tisch Observation/Short Stay Unit, NYU Langone Health, New York, New York. At the time of writing, she was health policy fellow, Robert Graham Center, Washington, DC; [email protected]Gillian Bartlett-Esquilant, PhDProfessor and associate chair research director, Department of Family Medicine, McGill University, Montreal, Quebec.Robert L. Phillips, MD, MSPHExecutive director, Center for Professionalism and Value in Health Care, Washington, DC.
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 | 0,014 | 0,097 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,009 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,046 | 0,062 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,006 |
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 ».