Bibliographic record
Abstract
The 13 million nurses worldwide constitute most of the global healthcare workforce and are uniquely positioned to engage with others to address disparities in healthcare to achieve the goal of better health for all. A new vision for nurses involves active participation and collaboration with international colleagues across research practice and policy domains. Nursing can embrace new concepts and a new approach-"One World, One Health"-to animate nursing engagement in global health, as it is uniquely positioned to participate in novel ways to improve healthcare for the well-being of the global community. This opinion paper takes a historical and reflective approach to inform and inspire nurses to engage in global health practice, research, and policy to achieve the Sustainable Development Goals. It can be argued that a colonial perspective currently informs scholarship pertaining to nursing global health engagement. The notion of unidirectional relationships where those with resources support training of those less fortunate has dominated the framing of nursing involvement in low- and middle-income countries. This paper suggests moving beyond this conceptualization to a more collaborative and equitable approach that positions nurses as cocreators and brokers of knowledge. We propose two concepts, reverse innovation and two-way learning, to guide global partnerships where nurses are active participants.
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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.035 | 0.038 |
| Insufficient payload (model declined to judge) | 0.051 | 0.020 |
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".