The Contribution of American Nurses to the Evolution of the International Council of Nurses
Bibliographic record
Abstract
As the nursing profession celebrates the International Year of the Nurse and Midwife, it is time to take stock of the contribution that American nurses and the United States have made to the evolution of the International Council of Nurses (ICN). American nurses were involved even before the conception of the organization and have played a significant role in its leadership and development. Nurses who have been active in the American Nurses Association (ANA) have often been heavily involved in various aspects of ICN governance and evolution. Additionally, several American philanthropic foundations and corporate donors have supported a wide range of ICN activity that has helped advance the nursing profession around the world. As we celebrate Nightingale’s legacy, we should also think about all the nurses who have brought us to this point from the past, and those collaborating today and tomorrow. Examining the contribution of American nursing highlights the fact that this collaborative effort of the world’s nurses is needed if we are to optimize access to services, quality of care and sustainability of the nursing profession.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".