Factors influencing nurse‐assessed quality nursing care: A cross‐sectional study in hospitals
Bibliographic record
Abstract
AIM: To propose a hypothesized theoretical model and apply it to examine the structural relationships among work environment, patient-to-nurse ratio, job satisfaction, burnout, intention to leave and quality nursing care. BACKGROUND: Improving quality nursing care is a first consideration in nursing management globally. A better understanding of factors influencing quality nursing care can help hospital administrators implement effective programmes to improve quality of services. Although certain bivariate correlations have been found between selected factors and quality nursing care in different study models, no studies have examined the relationships among work environment, patient-to-nurse ratio, job satisfaction, burnout, intention to leave and quality nursing care in a more comprehensive theoretical model. DESIGN: A cross-sectional survey. METHODS: The questionnaires were collected from 510 Chinese nurses in four Chinese tertiary hospitals in January 2015. The validity and internal consistency reliability of research instruments were evaluated. Structural equation modelling was used to test a theoretical model. RESULTS: The findings revealed that the data supported the theoretical model. Work environment had a large total effect size on quality nursing care. Burnout largely and directly influenced quality nursing care, which was followed by work environment and patient-to-nurse ratio. Job satisfaction indirectly affected quality nursing care through burnout. CONCLUSIONS: This study shows how work environment past burnout and job satisfaction influences quality nursing care. Apart from nurses' work conditions of work environment and patient-to-nurse ratio, hospital administrators should pay more attention to nurse outcomes of job satisfaction and burnout when designing intervention programmes to improve quality nursing care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".