Introduction: Shining the Light on Asian American, Native Hawaiian, and Pacific Islander Health
Bibliographic record
Abstract
The United States' diverse Asian American and Native Hawaiian and Pacific Islander (AA and NHPI) populations have grown faster than those of any other racial/ ethnic group over the past three decades.*Out of the shadows and into the light, the health and health care issues faced by our AA and NHPI communities across the U.S., its territories, and freely associated states matter more and more to the vitality and future of the nation.In 2015, we mark the 30th anniversary of the Heckler Report, 4 the seminal Report of the Secretary's Task Force on Black and Minority Health documenting national health inequities by race and ethnicity, which led to the establishment of the Office of Minority Health by Congress in 1986.5 Notably, the report concluded that Asian/Pacific Islanders in aggregate were healthier than any other racial group in the U.S. In this supplement, Ponce and colleagues † retrace the story of the first national AA and NHPI data initiatives and key milestones for data equity that were established as a direct response to this report, and specifically to dispel the so-called model minority myth, to strengthen AA and NHPI voices, and to advance federal efforts to promote health issues facing AA and NHPI communities. Indeed, the Heckler Report spawned the creation of two of our communities' national institutions, the Asian & Pacific Islander American Health Forum (APIAHF) and the Association of Asian Pacific Community Health Organizations (AAPCHO).Over the past 30 years, tremendous strides have been made in documenting and monitoring persistent and increasing health inequities disfavoring AAs and NHPIs and the critical steps needed to address gaps in the evidence base to focus on unequal health by race, ethnicity, language, and other social determinants.[6][7][8][9][10][11] Ko Chin and Caballero* present a community perspective on the leadership of Assistant Secretary for Health, Dr.Howard Koh, and his work in shepherding new national health equity initiatives, including the Patient Protection and Affordable Care Act of 2010, the reauthorization of the Office of Minority Health (OMH), the creation of the first national U.S. Department of Health and Human Services (HHS) Plan for Asian American, Native Hawaiian, and Pacific Islander Health, 12 and the new HHS data standards for race, ethnicity, sex, primary language, and disability status from Section 4302 of the Affordable Care Act (ACA).13,14 In addition, the National Standards for Culturally and Linguistically Appropriate Service in Health and Health Care were updated
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".