Deconstructing Racialized Experiences in Healthcare: What a Missed Opportunityfor Healing Looks Like and Healthcare Resources for Children and Their Families
Bibliographic record
Abstract
Some patients and families of color, including Asian Americans, face significant adverse stressors due to living within a White-dominant society. Xenophobia and racism can impact health. Research evidence points to early exposure to adverse childhood experiences such as racial discrimination as being detrimental and having significant short-term and long-term impact on physical and mental health. The purpose of this commentary article is to illuminate the need of patients and their families who may seek health care providers (HCPs) to express their concerns and fears when issues of xenophobia and racism arise. Patients and families need space in a healthcare setting to feel heard and understood. Anti-Asian xenophobia and racism among medically underserved Asian Americans persists and has been heightened during the COVID-19 pandemic. We describe tenets of Critical Race Theory and AsianCrit, and use this lens to understand an example actual scenario, a counter-story, of a Vietnamese mother, and her Vietnamese-Chinese American family's experience with xenophobia and racism at a community recreation center and the subsequent communication of this experience with a HCP. We describe the impacts of these experiences of seeking healing including discontinuity of a HCP-patient-family relationship. It takes bravery for patients and families to tell their story of xenophobia and racism to a HCP. There are Asian Americans who are afraid to seek healthcare because of anti-Asian xenophobia and concerns about White fragility. Following, we highlight research evidence on implicit bias, also known as unconscious bias, as context about its persistent and widespread existence among healthcare professionals in general and the need to address this in healthcare. Implicit bias can influence care provided to a patient-family and the interactions between a HCP-patient-family. We include additional resources such as those from the National Association of Pediatric Nurse Practitioners, American Psychological Association Office on Children Youth and Families, the Office of Ethnic Minority Affairs, the Office on Socioeconomic Status, and American Academy of Pediatrics to consider in support of equity in healthcare practice of children and their families.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".