Hospital staffing, organization, and quality of care: cross-national findings
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of nurse staffing and organizational support for nursing care on nurses' dissatisfaction with their jobs, nurse burnout, and nurse reports of quality of patient care in an international sample of hospitals. DESIGN: Multisite cross-sectional survey. SETTING: Adult acute-care hospitals in the United States (Pennsylvania), Canada (Ontario and British Columbia), England, and Scotland. STUDY PARTICIPANTS: 10 319 nurses working on medical and surgical units in 303 hospitals across the five jurisdictions. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Nurse job dissatisfaction, burnout, and nurse-rated quality of care. RESULTS: Dissatisfaction, burnout, and concerns about quality of care were common among hospital nurses in all five sites. Organizational/managerial support for nursing had a pronounced effect on nurse dissatisfaction and burnout, and both organizational support for nursing and nurse staffing were directly, and independently, related to nurse-assessed quality of care. Multivariate results imply that nurse reports of low quality care were three times as likely in hospitals with low staffing and support for nurses as in hospitals with high staffing and support. CONCLUSION: Adequate nurse staffing and organizational/managerial support for nursing are key to improving the quality of patient care, to diminishing nurse job dissatisfaction and burnout and, ultimately, to improving the nurse retention problem in hospital settings.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".