Notice bibliographique
Résumé
As medical students, we are taught to consider the patient, not simply the diagnosis. This concept, known as physicianship, encourages us to validate and dignify the lives of our patients while empathically looking for pertinent clues and findings to diagnose their condition. Despite its inherent humanism, we often hesitate to practice physicianship because, in our clinical experiences, carrying on a conversation with a patient does not extend far beyond the science of his or her condition. During medical school, an encounter with a patient, Mr. O, led me to realize that the distinction between science and physicianship is not always clear and that, in medicine, the two often overlap. On an otherwise typical Tuesday afternoon in the clinic, I was assigned to see the last patient of the day, Mr. O. As usual, I entered the exam room and said hello. Mr. O was a 78-year-old gentleman who appeared quite healthy upon my initial exam. I spent the next 30 minutes completing my duties as a medical student—taking a history, checking his vital signs. During this time, I did most of the talking. I learned that, since his last visit, Mr. O had achieved his weight loss goal and had regained full range of motion in his previously injured knee. Despite the good news, I recognized that Mr. O still was dissatisfied. I doubted that his dissatisfaction was with me or with our recent interaction. Instead, I sensed that he was looking for someone simply to listen to him. Was it possible that not one of his doctors had taken the time recently to listen to what he had to say if what he had to say wasn’t the answer to a question on his chart? Because I had extra time before my supervisor would be ready to review Mr. O’s case with me, I decided to spend that time with Mr. O in the hopes of remedying his apparent loneliness in addition to his physical ailments. I started with the question “What do you like to do for fun?” Out of nothing, something remarkable evolved. For the next half hour, Mr. O did all of the talking. Uncontrollably, he spoke, yelled, cried, and laughed, ridding himself of some of the pain that he had carried for so many years. Slowly, he slid his hand into his pocket and retrieved the list of his prescriptions. He stared intently at the name tag that hung from my white coat and began to write my name on the same piece of paper. He hugged me and politely asked, “Can I add you to my list of medications?” If I ever doubted the merits of physicianship, Mr. O reinforced them in a way that I will never forget. That day, I thanked God not for my voice or for my name but for having blessed me with the ability to listen. I learned how simple caring for patients could be—just be there to listen to them. As medical students, we may not be licensed to write prescriptions yet, but our white coats are, more often than not, the safest, most powerful medication that our patients need. Author’s Note: The name in this essay has been changed to protect the identity of the patient.
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 | 0,010 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,001 | 0,014 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,092 | 0,017 |
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 ».