Medical education interventions influencing physician distribution into underserved communities: a scoping review
Notice bibliographique
Résumé
BACKGROUND AND OBJECTIVE: Physician maldistribution is a global problem that hinders patients' abilities to access healthcare services. Medical education presents an opportunity to influence physicians towards meeting the healthcare needs of underserved communities when establishing their practice. Understanding the impact of educational interventions designed to offset physician maldistribution is crucial to informing health human resource strategies aimed at ensuring that the disposition of the physician workforce best serves the diverse needs of all patients and communities. METHODS: A scoping review was conducted using a six-stage framework to help map current evidence on educational interventions designed to influence physicians' decisions or intention to establish practice in underserved areas. A search strategy was developed and used to conduct database searches. Data were synthesized according to the types of interventions and the location in the medical education professional development trajectory, that influence physician intention or decision for rural and underserved practice locations. RESULTS: There were 130 articles included in the review, categorized according to four categories: preferential admissions criteria, undergraduate training in underserved areas, postgraduate training in underserved areas, and financial incentives. A fifth category was constructed to reflect initiatives comprised of various combinations of these four interventions. Most studies demonstrated a positive impact on practice location, suggesting that selecting students from underserved or rural areas, requiring them to attend rural campuses, and/or participate in rural clerkships or rotations are influential in distributing physicians in underserved or rural locations. However, these studies may be confounded by various factors including rural origin, pre-existing interest in rural practice, and lifestyle. Articles also had various limitations including self-selection bias, and a lack of standard definition for underservedness. CONCLUSIONS: Various educational interventions can influence physician practice location: preferential admissions criteria, rural experiences during undergraduate and postgraduate medical training, and financial incentives. Educators and policymakers should consider the social identity, preferences, and motivations of aspiring physicians as they have considerable impact on the effectiveness of education initiatives designed to influence physician distribution in underserved locations.
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 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».