Nurses Speaking Up for Mothers and Children: 25 Years of Public Policy Involvement
Bibliographic record
Abstract
Public policy decisions directly affect the health care of women and children and also affect the practice of maternal and child nursing. The past quarter century has seen a shift in nursing involvement in the public policy process. Heightened awareness of the collective power of nurses, greater independence of the nursing profession, the increasing capability for generating research to guide the formulation of public policy, and nurses' better understanding of the political process have all contributed to the increasing influence of our nation's 2.6 million nurses. The passage of several significant pieces of legislation, such as expansions of the Medicaid program for pregnant women and children in the late 1980s, have opened up new opportunities for nurses to further shape the nation's health care agenda for women and children. Nurses can and should become more involved with the policy-making process at local, state, and national levels to assure that decisions are made that benefit this important population group. Leadership in the public policy arena will give nurses the best opportunities for putting forth the agendas that will accomplish these goals.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".