The nurse outcomes and patient outcomes following the High‐Quality Care Project
Bibliographic record
Abstract
BACKGROUND: There have been many single cross-sectional studies on nurse or patient outcomes. However, long-term evidence on improving nurse and patient outcomes is still limited. The High-Quality Care Project is a national project in China for improving nurse and patient outcomes by implementing primary nursing. AIM: (1) To assess the long-term changes in nurse and patient outcomes in the context of the High-Quality Care Project. (2) To explore the potential influences of primary nursing on nurse and patient outcomes based on this study and broader existing evidence. METHODS: The data of two cross-sectional studies were used for analysis. The two cross-sectional studies were conducted before (2009) and after (2016) the High-Quality Care Project. A total of 1376 nurses and 904 patients from 40 units of 10 tertiary hospitals were surveyed. Reliable and validated instruments were used to measure nurse and patient outcomes. Multilevel modelling was the main method for data analysis. RESULTS: Nurses in 2016 were more satisfied than nurses in 2009 with most dimensions of nurse work environment and job satisfaction. However, they were not more satisfied with burnout, global job satisfaction or intention to leave their job. Nurses in 2016 also reported better quality of patient care and patient safety while their patients reported higher patient satisfaction. CONCLUSION: The analysis of our results based on existing evidence indicates that primary nursing could be considered as a potentially effective way to improve nurse work environment and patient outcomes. More studies with rigorous study design from micro perspectives would be useful to further explore the direct effects of primary nursing on nurse or/and patient outcomes. IMPLICATIONS FOR NURSING AND NURSING POLICY: Policymakers, healthcare service leaders and nurse managers should make efforts to provide multi-level supports to cultivate an encouraging environment for nurses to practice primary nursing, because the implementation of primary nursing may improve the nurse work environment and patient outcomes. Furthermore, improving nurse participation in hospital affairs and developing nursing discipline and education for increasing nursing staff resource and nurses' capacity - which all need policy and management supports - are crucial to further improve nurse and patient outcomes.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".