Notice bibliographique
Résumé
It was 7 AM, and we were clamoring onto the back of an open pickup truck. We were two medical students, spending the summer in Pierre Payen, a small town on the west coast of Haiti. It had not taken us long to become accustomed to the stifling heat, the Haitian Creole, and the dark nights filled with barking dogs and confused roosters. Traveling into town that morning, we stopped to pick up Dr. Joseph, a young, confident doctor who had graciously taken us under his wing. That day, we were heading into the mountains to a rural village, where international donors had established a small clinic. Dr. Joseph made the two-and-a-half-hour drive to this village once a week to see patients. Heavy rains the night before had turned the “road” into a dense muddy swamp. Along the way, we stopped outside modest homes to pick up our colleagues for the day—two nurses, a pharmacy technician, and two groundskeepers. The morning clinic was quieter than usual—The rains had deterred patients who normally would have had no qualms about a two-hour walk from their villages to see the doctor. As the clinic rooms filled, we moved between patients, watching Dr. Joseph uncover subtle physical findings with no more than his expertise and a stethoscope. Through our care passed febrile babies to be tested for typhoid and malaria, pregnant women with urinary tract infections, and elderly men with arthritis. They all left the clinic that day with reassurance, basic medications, and instructions to return should their condition not improve. By mid-afternoon, we were tired and hungry, but we watched as Dr. Joseph continued to approach each new patient with tireless empathy, curiosity, and equanimity. Not once did we hear him complain about his working environment or speak negatively of a patient or a colleague. During our ride back to town that evening, we listened to Dr. Joseph talk about what awaited him upon his return. In watching him perform expertly and compassionately as a general practitioner in a small rural clinic, we had forgotten that he was returning to town to continue his work as a general surgeon in an operating room. Humbled by the breadth of his medical knowledge and inspired by the compassionate care he provided, we reflected quietly as we rumbled along the muddy road. We were sweaty, tired, and a little carsick, but watching him work had rekindled the same fire and excitement about medicine that we felt only a few years ago when we received our acceptance letters to medical school. We had traveled to Haiti in search of an exciting clinical experience. What we learned from Dr. Joseph, though, extended far beyond the clinical symptoms of typhoid fever and malaria. He reminded us of the core values that we needed to practice good medicine. Throughout the remainder of our training, we will attempt to model the way he treated each patient with compassion, despite challenging surroundings. We will resist the urge to complain about minor delays or gaps in our health care system, instead focusing on its strengths. Finally, we will remember that it is a great privilege to be a medical student. As we proceed with our training, we can only hope that the lessons we learned during our time in Haiti—how to stay grounded and the importance of empathy, compassion, and optimism—will stay with us in the years ahead. Authors’ note: The name in this essay has been changed to protect the identity of the physician. Emily C. Wilson, MSc, and André R. Maddison, MSc
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 | 0,009 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,017 | 0,019 |
| Communication savante | 0,018 | 0,017 |
| Science ouverte | 0,002 | 0,019 |
| Intégrité de la recherche | 0,006 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,070 | 0,036 |
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 ».