Interdisciplinary Nursing Research Forum Successfully Held in Hong Kong
Bibliographic record
Abstract
The “Interdisciplinary Nursing Research Forum,” jointly organized by the School of Nursing at Peking University, the Interdisciplinary Nursing Research (INR) journal, and the Nethersole School of Nursing at the Chinese University of Hong Kong, was successfully held in Hong Kong from October 20 to 24, 2025. With the theme of “Integration and Innovation: Empowering Nursing,” the forum brought together experts and scholars from nursing and related fields around the world to discuss innovative interdisciplinary research for addressing global health challenges. The opening ceremony of the forum was hosted by Jing Zhou, Deputy Dean of the School of Nursing at Peking University. Professor Zhiwen Wang, Director of the Center for Evidence-Based Nursing Research at Peking University Health Science Center and Associate Editor-in-Chief of INR, pointed out that integration and innovation in nursing are crucial strategic measures for addressing global health challenges and are essential for the high-quality development of the nursing discipline. Professor Wai Tong Chien, Director of the Nethersole School of Nursing at the Chinese University of Hong Kong, emphasized that nursing is evolving from a “terminal role” in health care services to a technology-led force throughout the entire process of disease prevention, diagnosis, treatment and rehabilitation, showcasing a new landscape of disciplinary integration. The forum featured one main session and two specialized sub-forums: “Smart Health and Eldercare” and “Evidence Translation and Application.” Experts and scholars from universities in the United Kingdom, the United States, Canada, Thailand, the Philippines, Singapore, and mainland China engaged in profound discussions on cutting-edge topics, including the innovative application of Artificial Intelligence in chronic disease management, the development of virtual simulation technology for nursing education and practice, and the application of digital and intelligent technology to rehabilitation nursing. The participating experts agreed that interdisciplinary integration is essential for innovative development in nursing and critical for addressing future health challenges. The forum provided a high-level academic exchange platform for nursing scholars around the world, particularly young researchers. The atmosphere was dynamic, and the discussions were lively, showcasing the vitality and frontier trends of nursing research. The successful conclusion of this forum marks a significant step forward in international cooperation for interdisciplinary nursing research. The School of Nursing at Peking University and the INR journal have stated their commitment to continuously building an open and shared international academic platform, driving disciplinary crossover and scientific innovation, and contributing Chinese expertise to the high-quality development of the global nursing profession. Conflict of interest disclosures The authors declare that they have no financial conflict of interest with regard to the content of this report.
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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 | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".