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Enregistrement W156292065

Room for a view : A transformation

2001· article· en· W156292065 sur OpenAlexvenueno aff
Kerri Lambert

Notice bibliographique

RevueCanadian Medical Association Journal · 2001
Typearticle
Langueen
DomaineHealth Professions
ThématiqueChild and Adolescent Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMistakeConversationMedicineValue (mathematics)PsychologyPediatricsPsychoanalysisLawCommunication
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

I am not like most doctors. The marvels of medicine do not fascinate me. Rare syndromes and abnormal findings do not inspire me. Physiology and pharmacology do not leave me breathless. I do not like medicine. As I force myself out of bed each morning and put on my happy face for work, I am convinced that I should quit. Yet somehow I make it through the day. I return home physically and mentally exhausted. My extracurricular activities have become a chore. I let them slide, which is a mistake, since I clearly need an outlet. Nonmedical friends tell me to give it up and go back to teaching. My medical friends cannot sympathize, since their lives are just as hellish, or worse: some are in surgery. Acquaintances and relatives can't believe I would consider leaving this noble profession after I've “come so far.” I'm in a pediatrics residency. I like everything about it except the medicine. I love what most people dislike. I love developmental, behavioural and psychosocial pediatrics. I love to play, to read and to cut and paste with children. Unfortunately, a busy service, many admissions, presentations, journal clubs and mock oral exams leave little time for play in or outside the hospital. I remain in pediatrics because I see things somewhat differently. I value emotional medicine more than the science of disease. A recent conversation with one of our patients troubles me. He was being worked up for subacute bacterial endocarditis. This adorable four-year-old looked healthy but continued to spike temperatures. Our team entered his room to listen to his murmur. He welcomed us with a smile and laughter. When the third person (of five) was listening to his heart, he was told we needed more blood. He was going to get poked. He began to whimper. You're going to take blood? Why? Are you going to take it now? Will it hurt? Can my Mommy stay with me? What will it feel like? Are you going to do it now? Mommy, I don't want another needle! Can you do it tomorrow? What kind of band-aid will I get? His questions were limitless. We answered each one honestly. The medical student listening to his chest asked him nicely to please be quiet, so she could hear his heart. He complied for about 45 seconds and then began firing more questions. The next person to examine him prefaced her auscultation with a reminder to be quiet. I wish I had interrupted the clinical exam and tried to alleviate his fears. Instead, I only helped to stifle them, for both he and I stayed quiet. Guilt also plagues the next memory. Surgery was my first rotation as a medical student. I had just finished the first history and physical of my medical career. My patient had hepatomegaly. An abdominal ultrasound and CT showed hepatic cancer. The prognosis was poor: only six months to live. During morning rounds, our team of five walked into his room at 6:20. He was alone. His wife and family were at home. After asking him how he was feeling, the attending physician gave him the news. In front of five strangers, our patient found out he had cancer. “So you're sure it's cancer?” he asked. “I'm afraid so.” The team proceeded to the other side of the curtain to ask the next patient if he'd passed any gas. I next see my patient staring out a hallway window at 3 a.m. My first inclination was to leave him alone with his thoughts. But I turned back to talk to him. Selfishly, I needed to talk to him. I feel guilty at the memory of it: when we talked, he was trying to make me feel better. I feel guilty about the manner in which he was told of his impending death. I feel guilty knowing about his impending death. I still search obituaries for his name, although I know he has passed away. Medicine brings joy into millions of lives. Unfortunately, accompanying this joy is overwhelming hardship. This misery explains my attraction to psychosocial medicine. I interpret its sorrow with less morbidity. On our service is a teenager with pseudoseizures. Neurology has cleared her of any pathology. Her tox screen is negative. She is a top student who has a boyfriend, plays hockey, works two jobs, is bulimic and desperately wants to talk to her mother but does not know how. Her teenage angst has finally manifested itself in pseudoseizures. I find this fascinating from both a medical and a personal viewpoint. Rejecting my woes and shutting out the world is, at times, pathetically attractive. Sadly, catatonia has become enchanting. These thoughts make me want to quit and to remain in medicine at the same time. A wonderful senior resident once told me to look for something unique in each patient, to look for my own “take-home message.” For me, the breathtaking, heartstopping moments in medicine occur when I gain my young patients' trust and they let me play with them. In a profession that I permit to rob me of my energy and emotional strength, I hope to find stability. I hope to strike an equilibrium between my life and my profession, hoping that in this way I will find happiness. But if, in my search for serenity, I choose a path away from medicine, I will still treasure what I have gained: the ability to transform patients back into people.

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)0,015
score de la tête « metaresearch » (Gemma)0,033
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 enseignants0,043
Score d'incertitude au seuil0,143

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,033
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0300,038
Communication savante0,0300,032
Science ouverte0,0040,033
Intégrité de la recherche0,0090,031
Charge utile insuffisante (le modèle a refusé de juger)0,0430,020

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 Opus0,030
Tête enseignante GPT0,367
Écart entre enseignants0,337 · 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

Citations0
Publié2001
Routes d'admission1
Résumé présentoui

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