Bibliographic record
Abstract
Haeok Lee, PhD, RN, FAAN who is a Korean-American nurse scientist, received her doctor al degree from the Nursing Physiology Department, College of Nursing, University of California, San Francisco (UCSF), in 1993, and her post doctor al training from College of Medicine, UCSF. Dr. Lee worked at Case Western Reserve University and University of Colorado Health Sciences Center. She has worked at the UMass Boston since 2008. Dr. Lee has established a long-term commitment to minority health, especially Asian American Pacific Islanders, as a community leader, community health educator, and community researcher, and all these services have become a foundation for her community-based participatory research. Dr. Lee's research addresses current health problems framed in the context of social, political, and economic settings, and her studies have improved racial and ethnic data and developed national health policies to address health disparities in hepatitis B virus (HBV) infections and liver cancer among minorities. Dr. Lee's research, which is noteworthy for its theoretical base, is clearly filling the gap. Especially, Dr. Lee's research is beginning to have a favorable impact on national and international health policies and continuing education programs directed toward the global elimination of cervical and liver cancer-related health disparities in underserved and understudied populations.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.127 | 0.030 |
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".