Advocating healthy public policy: implications for baccalaureate nursing education.
Bibliographic record
Abstract
Advocating healthy public policy is increasingly recognized as an essential strategy for enhancing the health of populations. The purpose of this paper is to discuss the implications this priority area portends for the educational preparation of public health nurses (PHNs). Population health is central to public health nursing, and as such, it is imperative that PHNs employ policy advocacy strategies to influence positively the determinants that affect the health of populations. In this paper, we introduce the concept of healthy public policy and its relevance for public health nursing and baccalaureate nursing education. We outline substantive content areas that are fundamental to policy advocacy, such as determinants of health and their interrelationships, the policy process, and theoretical frameworks consistent with a socioenvironmental approach to health. In addition, we detail examples of specific learning experiences that provide students with opportunities to apply the content. Some of these activities include analysis of a population health issue, developing a position paper or resolution, writing letters to policy makers and the media, and working with lobbyists and policy makers.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 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".