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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".