2019 Asian American/Pacific Islander Nurses Association & Taiwan Nurses Association Joint International Conference: Changes in Nursing Research, Education, and Practice: From Local to Global
Bibliographic record
Abstract
The 2019 Asian American/Pacific Islander Nurses Association (AAPINA) & Taiwan Nurses Association (TWNA) Joint International Conference was held with the theme of Changes in Nursing Research, Education, and Practice: From Local to Global on August 16–17, 2019 at the Splendor Hotel, Taichung City, Taiwan. More than 700 researchers, educators, and clinical nurses from over 10 countries participated in the conference. A dozen of internationally well-known nursing scholars and leaders including Dr. Pamela F. Cipriono, the Vice President of International Council of Nurses (ICN), Dr. Wen-Ying Chou, the Program Director of Health Communication and Informatics Research Branch, the National Cancer Institute of the USA, and Dr. Eun-Ok Im, the AAPINA President-Elect, and Dr. Hsiu-Hung Wang, the TWNA President provided keynote speeches. In addition, seven international scholars and leaders provided a thought-provoking forum on changes in nursing leadership in Asian countries.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.013 |
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".