MétaCan
Menu
Retour à la cohorte
Enregistrement W2089687241 · doi:10.1097/01.acm.0000232422.81299.b7

Learning in Practice: Experiences and Perceptions of High-Scoring Physicians

2006· article· en· W2089687241 sur OpenAlexaff
Joan Sargeant, Karen Mann, Douglas Sinclair, Suzanne Ferrier, Philip D. Muirhead, Cees van der Vleuten, Job Metsemakers

Notice bibliographique

RevueAcademic Medicine · 2006
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésEnthusiasmPsychologyLaughterSocial psychologyMedical educationPsychoanalysisMedicine

Résumé

récupéré en direct d'OpenAlex

One day while presenting a patient to me, a medical student suddenly burst into tears while discussing the patient’s feet. Now, since feet usually are not tearjerkers (unless, of course, they are very pungent feet), it was clear that something was bothering her. Until then, the student had appeared reserved, quiet, and somewhat sullen, doing all her work, but lacking the same bright-eyed enthusiasm as her colleagues. However, after this emotional outburst, she revealed that her long-term relationship had just fallen apart. Now, while I could have easily switched the topic back to feet (which in general is not a good idea in most conversations), this was a natural segue to an open discussion about the challenges of maintaining relationships and medical careers. Suddenly the teaching session began resembling an Oprah Winfrey show as the other students and housestaff eagerly contributed their own relationship “war stories.” One resident related how he had kept falling asleep while spending time with his former girlfriend and could barely stay awake while she was breaking up with him. Another resident recalled how a rather cowardly former boyfriend ended their relationship suddenly by sending a text message to her pager while she was on call. Others talked about how hard it was to stay focused and project enthusiasm at school and work when their relationships hit potholes. Moreover, the rigors of training often prevented them from recognizing when their significant others were unhappy, needed something, cheated on them, or even completely changed their appearances. Many of us have gone through relationship difficulties during medical school, postgraduate training, and beyond. Those that haven’t are either very fortunate or need to get out of the hospital more often to actually have relationships. You can know all the medical facts in the world, but inability to manage your life outside training and work may impair your ability to be a good physician. After all, unhappy physicians often do not make very good physicians. So are we neglecting the “heart” when training future physicians? While many textbooks teach students and residents how to interpret blood gases or treat Lyme disease, none really tell them how to handle complicated relationship issues and the interplay between one’s medical career and personal life. Our value as educators comes from not only our scientific and medical expertise, but also insights from our own life experiences. In many cases, we can serve as role models personally as well as professionally. Sometimes frank discussions can help students and housestaff learn how to better deal with relationship issues. These discussions can also help us better understand the people that we are teaching and, in turn, make us better teachers. After all, what’s the use of focusing on the feet, when the problem is with the “heart.” Bruce Y. Lee, MD, MBA

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,335
Score d'incertitude au seuil0,322

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,367
Écart entre enseignants0,353 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations73
Publié2006
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAcademic MedicineMême sujetInnovations in Medical EducationTravaux en français237 207