Assessing the Impact of Community Factors on Acute Care Nurses’ Job Satisfaction and Turnover Intention
Bibliographic record
Abstract
BACKGROUND: Community factors may affect nurses' job behavior and decision making. There is a gap in the literature regarding the impact of community satisfaction, family ties, and community preferences on acute care nurses' turnover intention and job satisfaction. Furthermore, no studies have examined the differences in community satisfaction, community preferences, and family ties among nurses working in rural and urban settings. PURPOSE: To identify the impact of family ties, community satisfaction, and community preferences on turnover intention and job satisfaction among acute care nurses working in Ontario's urban and rural areas. METHODS: Descriptive correlational survey design was used in this study. A targeted stratified sampling technique was used to recruit acute care nurses working in Ontario's urban and rural areas (N = 349) between May 2019 and July 2019. Dillman's approach was used to guide data collection. Parametric and non-parametric tests were used for data analysis. RESULTS: A significant association was found between working settings and community preferences. A statistically significant positive relationship between community satisfaction and nurses' job satisfaction was identified. Furthermore, community satisfaction had a negative impact on turnover intention. Neither community preference nor family ties were significantly associated with turnover intention or job satisfaction. CONCLUSION: The study suggests that community satisfaction can influence important nurse work-related outcomes. Future studies should replicate and validate these results in different contexts and cultures. Retaining nurses may be difficult if they are not satisfied with their communities.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| 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".