Bibliographic record
Abstract
The 8 thNursing Symposium on Cancer Care, an event organized by the Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, was successfully held on May 24–25, 2018 in Hong Kong, China, under the theme “Composing future symphony of cancer care through collaboration, innovation, and transformation.” The Symposium, led by Professor Ka Ming Chow and Professor Cho Lee Wong, aims to provide an opportunity for international exchanges and discussions between healthcare providers, policymakers and academic researchers on the potential strategies to achieve innovation for the optimization of care for cancer patients worldwide. The events had attracted more than 300 individuals from a number of countries in the Asia-Pacific region, as well as countries in North America, Europe, and Africa. We have invited 20 overseas and local renowned speakers to deliver speeches at the Symposium. In particular, we are pleased to be able to invite Professor Greta G. Cummings from the University of Alberta and Professor Alexandra McCarthy from the University of Auckland as our keynote speakers for the event. Professor Cummings delivered a keynote address titled “Leadership in improving outcomes of cancer care through collaboration, innovation and transformation,” providing an update on previous research on leadership in nursing, and suggesting strategies in developing leadership skills among nurses. Professor McCarthy delivered a keynote speech titled “Eat well, move well, sleep well: Translating lifestyle research to the clinical cancer setting,” discussing how nutrition, physical activity, and sleep can affect cancer development and the effective strategies in enabling individuals to engage in healthy lifestyles to prevent cancer. In addition to the speeches presented by the invited speakers, there were 41 oral presentations and 60 poster presentations delivered by the Symposium attendees. These presentations were grouped into six themes related to oncology care, namely, cancer prevention and management, cancer screening and early detection, care delivery and symptom management, survivorship and psychosocial care, palliative and end-of-life care, innovation, and development in cancer care. These presentations, therefore, provided a great opportunity for Symposium attendees to gain a better understanding on the current developments in research into the ways of enhancing the quality of different facets of care across the cancer care continuum.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".