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Enregistrement W4210364205 · doi:10.1097/acm.0000000000002343

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2018· article· en· W4210364205 sur OpenAlexaboutno aff
Walter Klyce

Notice bibliographique

RevueAcademic Medicine · 2018
Typearticle
Langueen
DomaineHealth Professions
ThématiqueObesity and Health Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMemphisQuarter (Canadian coin)PovertyCharterMedicinePsychologyFamily medicineHistoryPolitical scienceLaw

Résumé

récupéré en direct d'OpenAlex

When I imagine American public health problems in the abstract, I see my hometown of Memphis, Tennessee. I picture a school system so broken that it voluntarily surrendered its own charter. I picture a city recently ranked as the second-most dangerous city in the United States, with more than 900 violent crimes per 100,000 residents. I picture a city that only just lost the title for poorest large metro area, with a child poverty rate over 40%. I picture a city that, staggeringly, is ranked first both in food insecurity and in obesity, with a quarter of its residents unable to buy food during the year, but with more than 36% being obese. I picture the menu at my sister’s favorite restaurant, where macaroni and cheese is, unironically, listed as a vegetable. This picture could not have felt more different from what I saw on my medical school’s family medicine rotation. At the end of two weeks in suburban New England, I had met exactly two people of color. The only language barrier I encountered was a minor difficulty understanding the accents of an older couple from Poland. Everyone drove to the clinic, because everyone had a car, because everyone had a job. Certainly, some of the problems I encountered were similar to what you’d see in Memphis. There, as everywhere, people had diabetes, hypertension, depression, pain. But because of the wealthy, whitewashed nature of the clinic, I see now that I had largely let my guard down. I didn’t expect these patients to be uneducated, to have drug addictions. I didn’t expect them to be racist. Blinded by my own biases, perhaps, I had forgotten that those problems, too, are universal. My mentor and I were seeing a septuagenarian with diabetes when this illusion was finally shattered. My mentor had just gotten back from a nutrition conference, and he was excitedly trying to sell his patients on a “plant-based diet,” which was code for “basically vegan.” This patient was more skeptical than most, so we took turns proposing whole foods he might find tolerable. When I suggested brown rice, he smirked derisively and said, “Yeah, but rice messes with your eyes, don’t it?” My mentor looked confused, so the patient placed his fingertips at the corner of his eyelids, pulled them into a squint, and said: “It makes ’em look like a Chinaman. I don’t want to have Chinaman eyes.” As a white male, I don’t know how outraged I’m allowed to get in response to a comment like that. I did anyway. Still, I wasn’t his doctor, and the visit was almost over. I was never going to see this guy again anyway. I could almost have forgotten the entire ugly discussion. But the patient, determined to be memorable, had one more barb up his sleeve. My mentor had to step out of the room, so I stuck around to keep him company. He asked where I was from, and I told him, “Memphis, Tennessee.” He said: “Y’know, I’ve always wondered something about the South. I was hoping you could clear it up for me.” Eager to bury the hatchet—and always interested in talking about my hometown—I said, “Sure.” Screwing up his face into the same wicked grin, he asked, “If a man and a woman get divorced in the South, are they still brother and sister?” For about the first time in my life, I said nothing. Fortunately, my mentor came back a few seconds later, and the patient was soon out the door. When we got to our desks, my mentor acknowledged the unfortunateness of the patient’s racist remarks, and we processed it, albeit superficially. (I don’t think he heard the second offense.) Still, I wondered to myself … knowing what I now did, would I be comfortable seeing this patient again? Could I rely on myself to give careful, considerate thought to his overall well-being? Would I be willing to be his doctor? While these questions are still hypothetical for me, they are likely to become real at some point in my career, and when that moment comes, I hope that I am ready to say yes. There’s a French saying for the witty thing you wish you’d said at the time but didn’t think of until you were halfway down the stairs. It’s called l’esprit de l’escalier: “staircase wit.” I’ve often suffered from such second-guessing, and this episode was certainly no different. My initial reaction was an immature wish to say something hurtful back to him, almost a sort of acting out to get his attention, maybe even wound him a bit to show him how he’d wounded me. I later concluded that sometimes the most satisfying wit is the type that’s meaningful only to oneself. If I could replay that scene, after the patient asked me about divorces in Memphis, I think what I’d say is: “Y’know, you’d be surprised how much you have in common with people in the South. You can travel the whole country, but, for better or for worse, people are pretty much the same wherever you go.” Acknowledgments: The author would like to acknowledge Professor Hedy Wald, Dr. Ed Feller, and the family medicine clerkship at Brown University.

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)0,001
score de la tête « metaresearch » (Gemma)0,004
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: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,566
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,001
Communication savante0,0050,004
Science ouverte0,0010,003
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,5660,260

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 Opus0,209
Tête enseignante GPT0,593
Écart entre enseignants0,384 · 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
GenreAutre

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
Publié2018
Routes d'admission1
Résumé présentoui

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