Bibliographic record
Abstract
Today's global health problems may seem insurmountable. Antibiotic-resistant microbes are increasing, and more economic, environmental, and social factors are affecting health. Health care costs keep rising. Hot politics and the chronic global nursing shortages all threaten the future of health care delivery. Also, diseases in many war-torn regions clearly place all humanity's health at risk. How can nurses possibly address these larger "global" challenges? To consider this question--and what nurses might do to contribute solutions--this article looks at the wider horizon of health care problems and how Florence Nightingale faced similar bigger health issues in her time. The health problems of today require renewed vision and the participation of committed citizens who take an active role in the promotion of human health-both locally and globally. By learning more about Nightingale's legacy, nurses actually attain a significant breadth and depth of knowledge and skill to share in these endeavors. Based on a review of Nightingale's responses and insights, seven recommendations are shared for consideration. While continuing the practices we have established, nurses can also create new, innovative, and relevant practice arenas, becoming--like she did in her time--global change agents for the sake of human health. From her broader viewpoint, Nightingale passed her global vision to us in order to extend our own horizons of possibility: remembering who we are, considering what we can do, who we care for, and why.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".