Evolution of an interdisciplinary curriculum.
Notice bibliographique
Résumé
Need for Interdisciplinary Rural Health Education Within the next decade, a growing elderly population that bears the burden of chronic illnesses will continue to challenge health practitioners with ever more complex health care needs. The number of patients with active human immunodeficiency virus (HIV) infection will continue to rise, necessitating expanded access to innovative medications. Increasing obesity and sedentary lifestyles will produce continued growth in the prevalence of diabetes, heart disease, and their associated sequelae. Blumenthal and Kagen1 state that persons living in the rural United States are more likely to experience higher rates of chronic diseases associated with health-damaging behaviors such as obesity and physical inactivity than persons in large metropolitan areas. Residents living in rural communities are more likely to smoke, have a sedentary lifestyle, lose their teeth due to nutritional deficits, and lack access to adequate health care services, leading to a higher rate of mortality than urban areas. Heart disease being noted as the leading cause of death in the United States, the Centers for Disease Control and Prevention reported that the South has a 20% increase in heart disease in rural communities compared with urban communities. Adults living in rural communities are more likely to smoke than those living in urban areas. The higher rates of chronic obstructive pulmonary disease found in rural communities are consistent with the use of cigarettes. In the South, poverty is higher in the rural areas than other geographic areas of the United States.2 Rural communities with limited resources to implement innovative health programs are particularly vulnerable to these problems. Another dilemma facing rural communities is that recent graduates from health professions schools increasingly are attracted to larger urban and suburban settings, often leaving rural communities to struggle with maintaining an adequate workforce. Another need, given the limited workforce and growing patient care demands, is improved interprofessional communication and collaboration. Many rural practitioners, overwhelmed by the burden of disease, are unable to make dramatic changes in community practice patterns to address system-wide problems. Special programs are needed that are designed specifically to address the unique need for a revitalized rural health workforce. In addressing this situation, Smith and Seymour3 believed it imperative that universities develop community-based interdisciplinary models of health professional education. The concept of interdisciplinary practice is not new. Interdisciplinary practice began in 1948 in the United States when a New York physician introduced the concept of interdisciplinary teams in providing home health care services.4 In 1991, a community partnership project sponsored by the W.K. Kellogg Foundation was developed at East Tennessee State University.3 During the same period, the federal government began its Interdisciplinary Rural Health Training Program with a variety of sites, including rural communities in Colorado, Arizona, New Mexico, Alabama, Hawaii, Oregon, South Dakota, Georgia, Maine, Kentucky, Michigan, and South Carolina. Dalhousie University Faculty of Health Professions in Canada used a similar educational model.5 Proponents of shared learning believe that interprofessional education enhances understanding of roles and responsibilities of other health professionals, develops skills in teamwork, and improves communication and interpersonal skills of participants. A commitment to interprofessional education begins with university faculty through their efforts in planning, organizing, and committing to this learning approach.6 Interdisciplinary Program at East Carolina University Beginning in 1993, the collaborative efforts of East Carolina University (ECU) School of Medicine, Eastern Area Health Education Center, other health science schools at ECU, and the rural communities of Duplin, Beaufort, and Bertie/Hertford/Gates counties established an interdisciplinary health professions training program in eastern North Carolina, with external funding from the U. …
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 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 | 0,001 | 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) | 0,000 | 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 ».