MétaCan
Menu
← Retour à la cohorte
Enregistrement W2320885619 · doi:10.1097/acm.0000000000000322

Reality Check

2014· article· en· W2320885619 sur OpenAlexaffabout
Matthew J. To

Notice bibliographique

RevueAcademic Medicine · 2014
Typearticle
Langueen
DomainePsychology
ThématiqueMigration, Health and Trauma
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésRefugeeFamily medicineMedicineHealth careSession (web analytics)PediatricsHemoglobin electrophoresisPsychologyAnemiaPolitical sciencePsychiatryLawComputer science

Résumé

récupéré en direct d'OpenAlex

Groggy medical students and a hematologist gathered around the table on a Monday morning for a tutorial session. We were there to discuss the case of Iman, a seven-year-old child who came to the clinic for a routine examination. He and his family had recently immigrated to Canada as refugees. As my colleagues and I began the discussion, we scrutinized lab results, which showed microcytic anemia. Our group discussed the differential diagnosis, then we collectively decided to order iron studies and a hemoglobin electrophoresis. The tests uncovered what we suspected, and a diagnosis of beta thalassemia minor was given to Iman. Ours were brief exchanges about different kinds of thalassemias, treatment options, and side effects. We asked questions and answered them. It seemed like a relatively simple exercise. As our time was winding down, we considered what impact our diagnosis would have on Iman and his family. I paused for a moment and scanned the patient description. Iman’s refugee status grabbed my attention. “I don’t think Iman would even have been diagnosed,” I blurted out. Some of my colleagues gave me puzzled glances. I explained: “I don’t think he would have received care under current Canadian regulations because many refugees are not eligible for routine medical examinations. The government recently made significant cuts to refugee health care.” Some of my colleagues understood what I was referring to and some looked surprised. The recent cuts prevented some refugees from receiving basic health services, like prenatal screening and routine medical examinations. Without health insurance coverage, many refugees were discouraged from seeking proper health care or were turned away at the clinic. My colleagues and I spent the last few minutes of the session talking about disparities in access to health care. Reflecting on this session, I realized that my colleagues and I had spent almost an entire hour talking about history taking, physical examination, lab tests, and treatment options, when in reality, Iman and his family would probably not even have come into the clinic because they lacked health insurance coverage. Reaching a correct diagnosis and discussing comprehensive treatment options were irrelevant if Iman and many other refugees in similar situations could not access the health care that they needed. Perhaps we should have started the session with a discussion about Iman’s refugee status and how that affected his access to health care and treatment options. From this experience, I gleaned that diagnosis and treatment of disease cannot be separated from the social context of our patients’ lives. In addition to the scientific evidence and clinical principles that we need to consider, we must not forget to look at the whole patient and consider how social context can impact health. Moreover, I realized that understanding the social determinants of health can provide valuable information to meet our patients’ unique health needs. Educating our selves about our patients’ health insurance coverage, access to health care, and policy changes are just as important as learning about the underlying causes and management of disease. Finally, being a good physician means not only learning about our patients’ social context but also advocating for changes—from the clinic to the community level—that will allow our patients to access quality health care, regardless of where they are from. Matthew J. To, BMSc Mr. To is a medical student, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; e-mail: [email protected]

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)0,003
score de la tête « metaresearch » (Gemma)0,034
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,572
Score d'incertitude au seuil0,611

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,034
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,002
Communication savante0,0060,006
Science ouverte0,0020,007
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,5720,310

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 Opus0,071
Tête enseignante GPT0,417
Écart entre enseignants0,346 · 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.

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
Publié2014
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueAcademic Medicine→Même sujetMigration, Health and Trauma→Travaux en français237 207→