Bibliographic record
Abstract
I am writing regarding the May Special Feature, “Nursing and the Sustainable Development Goals: From Nightingale to Now.” Throughout my nursing career, I have held the belief that nurses should be more proactive in preventing illnesses instead of just treating them. As nurses, we are taught to think of our patients in a holistic manner: mind, body, spirit, and environment. My question is, why are we not taught to think of our communities holistically? Nursing needs to adopt a new model that incorporates public health. Public health should be a central focus of nursing. Nurses must stand up to be leaders in the community and develop activities and educational and wellness programs to prevent diseases and address the 17 Sustainable Development Goals outlined in the article. We can accomplish this by collaborating with other professions within the community. A university in England implemented a public health improvement theme in its undergraduate nursing program to build a foundation of knowledge and skills to help drive change.1 Public health concepts should be studied in more depth in our nursing programs. Maybe by empowering young nurses with the knowledge needed to promote health, not just treat the sick, we can finally see a major change in disease prevention. Laura L. Williams, BSN, RN, CVRN-BC Angleton, TX
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.041 | 0.050 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".