Licensed Caregiver Characteristics and Staffing in California Acute Care Hospital Units
Bibliographic record
Abstract
PURPOSE: Concerns about declining quality of care and nurse staffing shortages led to legislation mandating minimum nurse-to-patient ratios in the state of California. Although research finds that better registered nurse (RN) staffing results in higher quality of care, little evidence exists on which to base specific nurse-patient ratios. The authors describe the results of a California survey characterizing licensed caregivers, identifying staffing levels by unit type, and describing how staffing levels vary across hospital types. METHODS: A stratified random sample of general acute care hospitals was surveyed to collect cross-sectional data on hospitals' nursing workforce and staffing practices and to assess the impact of potential patient-to-nurse staffing ratios. All academic medical centers; rural, private, and city/county hospitals; and hospitals affiliated with a large group-model health maintenance organization (HMO) were eligible for inclusion. RESULTS: Eighty hospitals were surveyed, representing all major metropolitan areas in the state. Acute care hospitals in California have diverse nursing staffs with variations in education, experience, and employment status. Considerable variations in skill mix were identified, with the proportion of RNs ranging from 30% to 84%, depending on the unit type surveyed. CONCLUSIONS: As states struggle with an anticipated critical shortage of RNs, these results have several implications for health and education policy. Future studies of this type will be needed to evaluate the impact of anticipated changes in the regulation of nurse staffing.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".