Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it