Notice bibliographique
Résumé
In addition to the usual information, such as procedures, logins, EMR operations, security passcards, and evaluation paperwork, covered on the first day of residency, my colleagues and I at the family medicine teaching clinic also want our residents to learn about our culture. And so the new chief resident, my codirector, and I gathered together a group of 20 or so orientation-weary R1s on their first day. I began, “This time of year is always a bit poignant. We've just said goodbye to our outgoing residents who have spent the past two years with us. We're excited to be starting new relationships with you. Over the years, we've found that about 20% of our graduating residents are lovely human beings, fantastic physicians, and great colleagues. You'll meet some of these individuals because we ask them to return as locums and, occasionally, as junior faculty. The majority of our grads, about 75% or so, are also great people, and we enjoy working with them. They make very good family physicians, and we keep in touch.” I paused for a second, noticing that several people were doing the math in their heads. “Frankly, because of the frustration and hostility they cause, we can't wait for the remaining five percent to leave. This orientation is to tell you what you need to know so that you don't find yourself in that five percent.” There was a long pause. Many of the residents stared at us in open shock; several mouths hung open. Then there was a round of nervous laughter. For the next 30 minutes, we shared how we hoped our residents would behave, as well as a few instances in which our residents strayed into “The Five Percent” in the past. Despite our shocking revelation, “The Five Percent” of residents is, in reality, a much smaller percentage of the group. And although we encounter residents who need remediation for academic and personal problems, they aren't the ones who incite any degree of animus. The ones who stick out in our minds are the residents who seem difficult—they complain, they create work for others, and they see patients as the enemy. In my experience, it's very difficult to address this pattern of behavior early enough so the resident may learn from his or her mistake without the incident being too emotional. Because of the deep frustration, even anger, these patterns of behavior can bring about in preceptors and peers, residents exhibiting these behaviors are often simply avoided. Or we focus instead exclusively on their cognitive evaluations because addressing how their attitudes and coping strategies affect others makes us uncomfortable. Finally, since we so often assume that others know what we expect of them (isn't it obvious?), our frustration is doubled when we see learners behave in ways that seem so deliberately annoying or concerning. This year's R1s have begun their rotation with a heightened awareness that their behavior really does impact other people and that first impressions on the team are important. Several have met with us, saying “I don't want to be in that five percent.” Best of all, if we do see something troubling, we have an easy way to introduce our concerns and start an early and corrective conversation. But I suspect we won't need to because they still remember what we said at orientation and won't easily forget their shock at our honesty. Cathy Risdon, MD, DMan
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 | 0,006 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,013 | 0,016 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,009 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,288 | 0,209 |
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 ».