Nurses’ Practice Environment and Their Job Satisfaction: A Study on Nurses Caring for Older Adults in Shanghai
Bibliographic record
Abstract
AIM: To examine the job satisfaction of nurses who are caring for older adults in healthcare settings in Shanghai, and to explore the underlying factors in order to explain and predict nurses' job satisfaction. BACKGROUND: China has the largest elderly population in the world, and its population is aging rapidly. Studies on job satisfaction of nurses providing care for the elderly in China can help to identify problem areas and develop strategies for the improvement of nurses' working conditions. However, to date, this subject matter has not been thoroughly studied in the Chinese context. Previous studies in other countries show that many factors impact nurses' job satisfaction, with the practice environment being a critical factor. There is a serious nursing shortage in China, especially in the big cities such as Shanghai. Given the increasing care demand of the aging population, learning about the job satisfaction level among nurses who are caring for older adults can provide essential information to help attract and retain nurses in this specialty area. METHODS: A cross-sectional survey was conducted among 444 nurses in 22 elderly care institutions in Shanghai. The Chinese version of the Index of Work Satisfaction (IWS) and the Nursing Practice Environment Scale were instruments used. Inferential statistical tests used to analyze the data included Spearman correlation analysis, one-way analysis of variance, and hierarchical regression tests. RESULTS: The average overall IWS (part B) score was 135.21 ± 19.34. Personality, job and organizational characteristics were found to be the most influential factors, and the practice environment was identified as having the strongest impact on job satisfaction (Beta = 0.494). CONCLUSION: Job satisfaction level among nurses who are caring for older adults in Shanghai is moderate, but the data suggest that this could be greatly increased if the nursing practice environment was improved.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".