Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".