Bibliographic record
Abstract
In many Western democracies, nursing consumes a comparatively large proportion of the health service budget and delivers the highest proportion of direct patient care. Therefore, identifying and representing the contribution of nurses to clinical effectiveness as well as the wider social benefit to populations and the economy is crucial. Predictive models on health and social care requirements for the next quarter of a century report a staggering shift in population age, multimorbidity, and complexity of need. This is leading to the widespread realization that change is needed to ensure that health care throughout the world meets the emerging needs of humankind. Currently, 97% of health budgets are spent on treatment, while only 3% are invested in prevention. Targeted initiatives that redistribute a higher proportion of national health policy budgets to the prevention of disease offer opportunities for nurses to address gaps in service provision. Nursing Now is a campaign focused on raising the status and profile of nursing globally while maximizing the contribution that nurses make to the health and well-being of individuals and communities. Nursing Now is a 3-year campaign, launched in 2018. The campaign has a very clear strategic goal to position nursing to optimize the profession's potential to fully contribute and make a real difference to the health of the global population.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.234 | 0.081 |
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".