Influencing and Shaping Policy Agendas: Advanced Practice Nurses Engagement with Global Organisations
Bibliographic record
Abstract
Global health, with its focus on health equity, will advance with the active involvement of advanced practice nurses (APNs). There are multiple venues, organisations, and groups that would benefit from APN involvement and contributions, including well-known international bodies like the United Nations, the World Health Organization, and UNICEF. There are other groups and organisations that APNs may be less familiar with but where advocacy and interaction can impact global health, like the UN Academic Impact, the International Labour Organisation, and others. There are opportunities for APNs within their professional organisations like the International Council of Nurses and others to take action. There are also individual opportunities with some groups where an APN can provide direct care. For all these opportunities, APNs will benefit from further knowledge about these organisations. For the major international bodies, this chapter provides an overview of the history and the complex structures. It also identifies existing organisations that have formal relationships as well as critiques of these bodies. Finally, the APN who wants to have direct care opportunities is cautioned to do so in a way that is ethical and sustainable.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".