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Enregistrement W2591363945 · doi:10.1097/acm.0000000000001545

Learning Professionalism Under Stress

2017· article· en· W2591363945 sur OpenAlexaff
Benjamin Chin‐Yee

Notice bibliographique

RevueAcademic Medicine · 2017
Typearticle
Langueen
DomaineHealth Professions
ThématiqueEthics in medical practice
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésCompassionHonestyEmpathyPsychologyAngerOffensivePatienceBurnoutBlameMedicineMedical educationSocial psychologyLawClinical psychology

Résumé

récupéré en direct d'OpenAlex

As a medical trainee, developing professionalism is a core goal of my education. Unlike technical and knowledge-based competencies, professionalism captures an all-encompassing attitude; it is more of a virtue than a skill. In an ideal world, the virtues of professionalism—honesty, integrity, commitment, compassion, respect, altruism—would govern all human interactions. However, in health care, professional attitudes are often challenged in emotional, stressful, and tiring situations. One particular patient encounter during my emergency medicine rotation highlighted this challenge. The young man arrived by ambulance at 5:00 am near the end of my overnight shift. He had been in an altercation and had suffered knife injuries to his face. He was intoxicated and belligerent; his swearing reverberated throughout the department, announcing his presence to us. As he was led into the ER, I noted multiple lacerations across his cheeks and forehead from which he was bleeding profusely. The nurses’ attempts to clean away the blood were met by offensive sexual comments. I approached the patient and our gazes met. “Quit looking at me like you want to fuck me!” he swore, jumping towards me. My initial shock gave way to anger, and I felt my blood boil with indignation. The staff physician intervened, conveying to the patient that his behavior was inappropriate and that he needed to cooperate to allow us to help him. The physician’s attempts to reason with the patient were only met with further curses and racial slurs. Ultimately, the patient was restrained and sedated so that we could attend to his injuries. When we reentered the room, we found him lying unconscious, intermittently groaning under heavy sedation. We proceeded to suture his wounds. In contrast to his previous aggressive demeanor, he now appeared pathetic and helpless. Any anger from our previous encounter had dissipated. Earlier, I had struggled to foster empathy while witnessing his abuses, but now, as he lay in restraints with torn clothing soaked in blood and dirt, I was suddenly overcome with feelings of guilt. I tried to imagine the circumstances that might have contributed to his current state. Despite our similar ages, I thought about how different our lives had been, about the privileges that I had enjoyed that he may have lacked. I felt guilty for my initial reaction, for having been angry, for judging him. This episode made me reflect on the challenge of remaining professional, especially in extreme situations where intense emotions and stress can cause us to forget the virtues of ethical practice and to revert to baser reactions. Being a physician certainly demands a high standard of ethical behavior. Nevertheless, this standard of professionalism does not necessarily entail a stoic notion of perfect equanimity. We all have human reactions and trying to eliminate these altogether, I believe, would harm our clinical practice. Just as anger and aversion can negatively impact patient care, joy, hope, and sadness can be harnessed to make for more meaningful doctor–patient relationships. This experience taught me that being a professional does not necessarily mean erasing all the negative emotions that one might feel. Instead, it involves developing the capacity to reflect on and to counteract initial reactions, recognizing how such feelings can adversely impact patient care. One strategy that helped me in this case was a deliberate and self-conscious attempt to foster empathy. The patient and I were, after all, not so different in age, perhaps separated only by luck and circumstance. As I continue in medicine, I hope that this experience will leave me better equipped to deal with these situations in a way that is not only professional but also human. Acknowledgments: The author wishes to thank Dr. Chris Willer, Dr. Sheena Taylor, and the student members of his Portfolio group for fruitful discussions on professionalism in medicine. He also wishes to thank the anonymous Faculty Scholar who provided feedback on this reflection and encouraged him to submit it for publication. Benjamin Chin-Yee, MA

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)0,005
score de la tête « metaresearch » (Gemma)0,017
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,017
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0080,010
Communication savante0,0100,005
Science ouverte0,0010,012
Intégrité de la recherche0,0030,009
Charge utile insuffisante (le modèle a refusé de juger)0,0130,005

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 Opus0,332
Tête enseignante GPT0,621
Écart entre enseignants0,289 · 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
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

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