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Enregistrement W2328536319 · doi:10.1097/acm.0b013e3182308da2

Beyond the Exam Room: A Call for Integrating Public Health Into Medical Education

2011· article· en· W2328536319 sur OpenAlexaboutno aff
Paul E. Jarris, Yumi Shitama Jarris, Ranit Mishori, Katie Sellers

Notice bibliographique

RevueAcademic Medicine · 2011
Typearticle
Langueen
DomaineHealth Professions
ThématiquePublic Health Policies and Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPublic healthContext (archaeology)CurriculumPopulationMedicineFamily medicineMedical educationPsychologyNursingPedagogyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Imagine a typical chronically ill patient who sees his doctor half an hour every three months. These four encounters each year—the physician's opportunity to counsel, diagnose, and treat—constitute only 0.02% of this patient's life. For all the rest—the 99.98% of the time that the patient is elsewhere, making decisions about his health in the context of his culture, family, and community—the doctor's impact on the patient's choices is minimal. Yet look at how narrowly we train doctors: four years of medical education focused almost entirely on what happens inside the exam room. Yes, those minutes are critical, but is it realistic to expect students to understand—from such a narrow perspective, in so little time—all the factors that affect their patients' lives? Have we prepared them to make sense of—much less influence—that critical 99.98%? That 99.98% belongs to community medicine, to population health, and to public health. Educators have already recognized the need to integrate more public and population health into medical education.1 In 2006, as part of an agreement between the Association of American Medical Colleges and the Centers for Disease Control and Prevention, 11 centers received funding to fully integrate population health into medical school curricula.1 More recently, the Liaison Committee on Medical Education added “public health sciences” to the requiredcurriculum (ED-11). Other U.S. institutions (e.g., the American Medical Association and the American Public Health Association2), as well as educators in Canada,3 have also advocated including more public health in training programs. These initiatives are an excellent start, yet in some ways they are not enough. Needs include addressing the differences between “public health” and “population health” (terms often used interchangeably) and ascertaining the competencies that we need to teach. Population health, often used to describe clinical preventive services measured at the level of the patient population or community, overlooks the policy, environmental, and social determinants of health that are critical components of public health. Many medical schools make a nod to concepts of epidemiology, environmental health, clinical preventive services, and community health. These are worthy topics, but for students to reach a fuller and more sophisticated understanding of the broad spectrum of public health, the curriculum must expand to include additional crucial interventions: changing social norms, creating healthy environments, developing public and private policy, and establishing laws that promote health. Take, for example, smoking—a true success story for public health. Public health policy led to higher tobacco taxes. Countermarketing campaigns helped change social norms, making smoking socially unacceptable to many. Clean indoor air laws created healthy environments and made lighting up inconvenient. Meanwhile, revised insurance policies covered nicotine replacement therapy, and solid evidence on the effectiveness of phone counseling for tobacco cessation led to the widespread creation of “quitlines,” providing management tools for clinicians and treatment options for patients. Through these interventions, the numbers of new smokers decreased, and current smokers' efforts to quit increased. Public health drove motivated individuals into the offices of prepared physicians. Patients benefited as a result of a comprehensive approach that operated within a social, legal, and policy framework. How well are we preparing tomorrow's doctors to address other high-priority health issues? Can we solve the obesity epidemic from within the exam room? Can anticipatory guidance improve the health of a child living within a food desert, in an environment where playing outside and walking to school are unsafe activities, where obesity is the norm? When we can alter public policy, social norms, and the environment, only then can we truly address the multiple factors that have created our obesogenic society. To prepare future physicians to be leaders in all aspects of health, we must give them tools that affect the underlying causes of illnesses. To do any less is to hamper their ability to improve patients' lives. Change takes time, but we cannot initiate change before we all agree on the broad scope of what public health entails. We should identify public health thinkers and innovative educators—partners from the fields of law, and public policy, from state and local health departments—with whom we can collaborate. We must also seek out model programs from within the United States and abroad. We should aim to embed and reinforce the public health model throughout the basic and clinical science years in medical school curricula, making sure students look beyond pathophysiology and traditional treatment options. Fostering a real attitudinal and philosophical paradigm shift will open up a whole realm of possibilities of causality and intervention, which will, in turn, have the greatest impact on the health of individuals and populations.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,055
score de la tête « metaresearch » (Gemma)0,092
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,102
Score d'incertitude au seuil0,289

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0550,092
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0020,005
Bibliométrie0,0040,002
Études des sciences et des technologies0,0180,022
Communication savante0,0220,052
Science ouverte0,0090,038
Intégrité de la recherche0,1020,105
Charge utile insuffisante (le modèle a refusé de juger)0,0780,016

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.

Tête enseignante Opus0,217
Tête enseignante GPT0,531
Écart entre enseignants0,314 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2011
Routes d'admission1
Résumé présentoui

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