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: Inconsistent findings in more than 100 studies have made it difficult to explain how variation in nurse staffing affects patient outcomes. Nurse dose, defined as the level of nurses required to provide patient care in hospital settings, draws on variables used in staffing studies to describe the influence of many staffing variables on outcomes. OBJECTIVES: The aim of this study was to examine the construct validity of nurse dose by determining its association with methicillin-resistant Staphylococcus aureus (MRSA) infections and reported patient falls on a sample of inpatient adult acute care units. METHODS: Staffing data came from 26 units in Ontario, Canada, and Michigan. Financial and human resource data were data sources for staffing variables. Sources of data for MRSA came from infection control departments. Incident reports were the data source for patient falls. Data analysis consisted of bivariate correlations and Poisson regression. RESULTS: Bivariate correlations revealed that nurse dose attributes (active ingredient and intensity) were associated significantly with both outcomes. Active ingredient (education, experience, skill mix) and intensity (full-time employees, registered nurse [RN]:patient ratio, RN hours per patient day) were significant predictors of MRSA. Coefficients for both attributes were negative and almost identical. Both attributes were significant predictors of reported patient falls, and coefficients were again negative, but coefficient sizes differed. DISCUSSION: By conceptualizing nurse and staffing variables (education, experience, skill mix, full-time employees, RN:patient ratio, RN hours per patient day) as attributes of nurse dose and by including these in the same analysis, it is possible to determine their relative influence on MRSA infections and reported patient falls.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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