Deconstructing the Monolith: An Educational Module for Understanding Disparities Within Asian American, Native Hawaiian, and Pacific Islander Populations
Bibliographic record
Abstract
Introduction: Asian American, Native Hawaiian, and Pacific Islander (AANHPI) people represent one of the largest and most rapidly growing groups in the United States and are often aggregated as a homogeneous, rather than diverse, population in medical research and education. Currently, few educational interventions focus on the disaggregation of AANHPI patient populations and the improvement of knowledge about health disparities that affect AANHPI patients. Methods: We developed, implemented, and facilitated a workshop for medical students to address AANHPI health disparities, adaptable for in-person and online formats. The 1-hour session involved a preworkshop evaluation; a PowerPoint presentation outlining the history of the Asian monolith bias, health disparities within AANHPI subgroups, and strategies for health care professionals and trainees to engage effectively with these communities; and a postworkshop evaluation. Pre- and postworkshop evaluations assessed participants' confidence and understanding of AANHPI health disparities. Additionally, the postworkshop evaluation gathered feedback on the presentation. Results: < .05). Whether attending virtually or in person, participants reported notable improvements in their self-evaluated confidence in treating AANHPI patients. Conclusion: The AANHPI patient population comprises a myriad of different cultures, historical contexts, and health needs. We present an educational module that is associated with significant improvement of knowledge about health disparities specific to this population, informing further efforts in cultural competence within medical education.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".