Global Perspectives on Nursing and Its Contribution to Healthcare and Health Policy: Thoughts on an Emerging Policy Model
Bibliographic record
Abstract
We know from rigorous evidence that nurses can exert an incredible impact on the everyday lives of people and their health. Nurses can also contribute in much wider spheres of influence by applying their knowledge and skills to address broader issues affecting population health across communities, nations and globally. Despite the prevalence of so many vexing health and social issues, nurses often fail to think globally, or even regionally, when they are lobbying for change. And while much political influence is local, some issues are simply too complex to rely on local influence alone. Importantly in all this, we must acknowledge the ways these complex health issues are shaped by economic and political agendas and not necessarily by healthcare agendas. As such, the nursing community has to act globally and locally, both within and outside of the nursing arena. This paper explores early thinking about an evolving model of spheres--or "bubbles"--of policy influence in which nurses can and must operate to more effectively impact key global health and healthcare challenges.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.022 | 0.036 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".