Notice bibliographique
Résumé
To the Editor: During a clinical encounter early in the COVID-19 pandemic, a patient commented, “So where in Asia did you train? Not near Wuhan I hope!” I answered the question simply with, “University of Toronto,” and moved on, but a spark of dissonance was ignited. While I recognize that my discomfort managing such microaggressions will pass with practice, I am concerned with the impact of such comments on my sense of professional and emotional worth and that of other trainees like me. My experience is not unique. The uncertainties of the pandemic have triggered multiple forms of intolerance toward Asian communities. Health care settings are not immune to the virulence of racism. Asian health care providers of all types have been victims of discrimination, ranging from having their origins and loyalties questioned to having patients refuse their care. The awkward smile and laconic response I use to avoid confrontation mask the progressive contributions those events can make toward moral distress and burnout in health care trainees. The reflective questions I asked myself after that clinical encounter are important to my future development, practice, and well-being. How can I, as a trainee of Asian descent, preserve my wellness while also learning to practice compassionate and inclusive medicine in the face of discrimination? Am I asking too much of myself? Theoretically, racism should be met with courageous, vocal, and reasoned opposition to promote a safe and inclusive clinical environment. Practically, as a junior learner, mustering up the courage to confront negative comments and behaviors from patients and colleagues is challenging. Recognizing and mentally processing the emotional impact of racism are both difficult, let alone responding appropriately in the moment. I am learning that these skills and strategies are not innate; they must be deliberately taught to and practiced by all trainees. The pandemic has presented health care providers with opportunities to train learners how to better defend their personal identities and protect their well-being, as well as those of their patients and colleagues. It has also challenged educators to explicitly introduce trainees to techniques and strategies that will allow them to stand up against racism and mistreatment in ways that do not negatively affect patient care. I am learning an essential lesson during this pandemic: Competent health care providers should not only appreciate the pathophysiology and epidemiology of COVID-19 but also be prepared to recognize and address the accompanying bigotry and hatred it can unleash toward minoritized populations. Acknowledgments: The author wishes to thank Dr. Joyce Nyhof-Young for her mentorship and guidance.
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 | 0,005 | 0,055 |
| 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 | 0,003 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,018 | 0,022 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».