Nurse to patient ratios in American health care
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
BACKGROUND: Nurses are employed in large numbers throughout health care. When their salary cost is considered as a percentage of total salary cost, they are arguably the most costly group of employees. Healthcare facilities have the potential to achieve large financial savings by reducing the number of nurses they employ. However, this may have negative consequences for staff, patients and the organisation as a whole. CONCLUSION: Research has shown that by reducing the number of nurses, patient outcomes deteriorate and length of stay increases. Curtailing nurse staffing levels can also lead to poor staff morale, nurse retention and recruitment problems and malpractice suits, which can raise costs far above the expense of employing more nurses. By reducing nurse to patient ratios, that is, by reducing the number of patients (see nurse to patient ratio box opposite), it is probable that patient care will improve along with patient satisfaction, poor morale will dissipate, fewer lawsuits will be filed and agency nurse use will decrease, all of which will help to reduce hospital costs in the long-term.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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