Medical Training Debt and Service Commitments: The Rural Consequences
Notice bibliographique
Résumé
This study assesses how student loan debt and scholarships, loan repayment and related programs with service requirements influence the incomes young physicians seek and attain, influence whether they choose to work in rural practice settings and affect the number of Medicaid-covered and uninsured patients they see. Data are from a 1999 mail survey of a national probability sample of 468 practicing family physicians, general internists and pediatricians who graduated from U.S. medical schools in 1988 and 1992. A majority of these generalist physicians recalled "moderate" or "great" concern for their financial situations before, during and after their training. Eighty percent financed all or part of their training with loans, and one-quarter received support from federal, state or community-sponsored scholarship, loan repayment and similar programs with service obligations. In their first job after residency, family physicians and pediatricians with greater debt reported caring for more patients insured under Medicaid and uninsured than did those with less debt. For no specialty was debt associated with physicians' income or likelihood of working in a rural area. Physicians serving commitments in exchange for training cost support, compared to those without obligations, were more likely to work in rural areas (33 vs. 7 percent, respectively, p < 0.001) and provided care to more Medicaid-covered and uninsured patients (53 vs. 29 percent, p < 0.001), but did not differ in their incomes ($99,600 vs. $93,800, p = 0.11). Thus, among physicians who train as generalists, the high costs of medical education appear to promote, not harm, national physician work force goals by prompting participation in service-requiring financial support programs and perhaps through increasing student borrowing. These positive outcomes for generalists should be weighed against other known and suspected negative consequences of the high costs of training, such as discouraging some poor students from medical careers altogether and perhaps influencing some medical students with high debt not to pursue primary care careers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».