Bibliographic record
Abstract
It is my honor to introduce you to the inaugural issue of Asian Pacific Island Nursing Journal.As an East-West Center Grantee during my doctoral education, I was exposed to many cultural groups from the East and their acculturation and assimilation issues.Most of the research at that time focused on the East with a few Western studies on cross-cultural methodologies and psychological and sociological areas such as emotions and marriage and family.There was little research specific to Asian Pacific health except in comparison to the majority group or a few seminal medical studies.Later, in my role as professor and associate dean for research at the University of Hawaii I noticed a great deal of interest in nursing related to culture and cultural differences in health care and individual responses to illnesses.Then, there was one journal dedicated to the health of this fastest growing population.My grants through the National Institute of Health institutes such as the National Institute for Nursing Research, the National Institute for Child Health Development, and the National Institute for General Medical Science, all focused on Asian and Pacific Islander health disparities.In addition, training grants such as the Minority Access to Research Careers and the Health Services Research Award for a PhD program focusing on Asian Pacific Islander development as scientists increased my awareness and need to share these findings.Another observation was that most studies combined and reported data on the different Asian and Pacific Islands cultures as if they were similar to each other, although each had specific and distinct health issues, needs, and responses.This
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.083 |
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".