Intergenerational partnerships in nursing: lessons from a plenary session at the 2019 International Council of Nurses' Singapore Conference
Bibliographic record
Abstract
This year's International Council of Nurses' global Congress in Singapore featured a theme of strengthening collaboration and partnerships across generations. In their plenary session, the two authors of this article exemplified this theme in both the development and delivery of their session. Together, they developed a set of 'common ground' attributes of nursing policy leaders, reflecting the knowledge and experiences of two very different nursing policy leaders: one a 'Baby Boomer' nurse with almost five decades of national and global policy leadership, and the other, an early career 'Millennial' leader engaged in her first decade of global policy leadership work. Their collaboration resulted in a session featuring reflections on relevance across generations, using symbolic images and a 'Ted-talk' style presentation, and active engagement of the audience. This article speaks to both the process for developing these 'common ground' attributes, and insights and lessons learned that can help inform future collaborations across generations of nurses.
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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".