Proceedings of the 2018 Asian American / Pacific Islander Nurses Association Conference: Local to Global--Future Directions for Research on Health Disparities
Bibliographic record
Abstract
The Asian American / Pacific Islander Nurses Association’s 15th Annual International Conference was held September 22-23, 2018 at the Hilton Garden Inn, located in Southpoint, Durham, S.C. with 43 research presenters. The conference theme was Local to Global: Future Directions for Research on Health Disparities with a dozen internationally well-known leaders around the world as keynote and special session speakers. Drs. Hyeoneui Kim, PhD, MPH, RN; Duke University School of Nursing and Jeeyae Choi, PhD, RN; University of North Carolina Wilmington School of Nursing were co-chairs and led the conference topics which addressed the current trends and future directions of health disparity research among AAPIs. The abstracts published here represent concurrent sessions focusing on the topics of: Nurses as Leaders- from Bedside to Board Room Moderator; Emerging Evidence and Future Directions of Clinical Practice; Paradigm Shift in Nursing Education to Influence Patient and Staff Safety and Patient Care Outcomes; Culturally Tailored Chronic Disease Care to Improve Patient Satisfaction and Patient Care Outcomes; The Use of Technology in Nursing Research; and Leveraging Technology for Practice, Research, Education. As the only journal focusing on (API) health and nursing we hope these topics give a flavor of areas important to API.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 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".