Bibliographic record
Abstract
BACKGROUND: Much attention is being paid to the adequacy of nurse staffing in acute care hospitals, and much of the information relies on nurses' perceptions about staffing adequacy. Yet, we know little about what influences these perceptions. OBJECTIVES: We examined the impact of hospital characteristics, nursing unit characteristics, nurse characteristics, and patient characteristics on nurses' perceptions of staffing adequacy. We tested three different models, incorporating different conceptualizations that relate current and past characteristics to these perceptions. METHOD: This was a secondary analysis of data from the Outcomes Research in Nursing Administration Project, a longitudinal study conducted in 60 hospitals in the Southeastern United States. RESULTS: Perceptions of staffing adequacy were influenced significantly by the hospital's case mix index and growth in hospital admissions, by the number of beds on a unit, and by patient acuity. Further, current perceptions of staffing adequacy were significantly affected by prior perceptions. CONCLUSION: Based on our results, we present potential interventions for administrators that may ameliorate some of the negative influences on nurses' perceptions of staffing adequacy.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".