Making a difference by serving in public office: Why we need more nurses in politics
Bibliographic record
Abstract
ABSTRACT: Nursing is the most trusted profession in the United States. Most nurses have the innate desire to care for others and make a difference in people's lives. Nurses are highly educated through rigorous programs that teach not only the physical sciences but also accountability, responsibility, and duty. Similarly, elected officials are accountable, have a duty to represent their constituents, and keep their best interests at the forefront of their agenda. The characteristics of nurses include higher education, integrity, responsibility, and compassion. Each one of these elements rests on the other to build a solid foundation of leadership. Nurses are natural leaders, and government needs more nurses to get involved and provide leadership benefits to our communities, including the local level where political decisions affect us all more personally. One of the responsibilities of nursing is to take an active role in politics and policy development. Nurses are a group of extraordinary individuals who are well suited to lead through elected office and influence policies that externally shape our practice and the well-being of our patients.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".