Factors affecting job satisfaction among acute care nurses working in rural and urban settings
Bibliographic record
Abstract
AIMS: To: (a) identify the differences and similarities in the extrinsic and intrinsic factors that influence job satisfaction among nurses in urban and rural Ontario; and (b) determine the impact of job satisfaction on nurses' turnover intention among nurses working in rural and urban settings in Ontario. DESIGN: Cross-sectional correlational design was used for this study. METHODS: Data were collected between May 2019-July 2019 in southern Ontario. Participants (N=349) completed the Acute Care Nurses' Job Satisfaction Scale and The Anticipated Turnover Scale. A stratified sampling technique was used for recruiting the sample population and participants were given the option to respond either online or by mailed survey. RESULTS: There was no significant difference between rural and urban nurses in either overall job satisfaction level or turnover intention. Peer support/work conditions, quality of supervision, and achievement/job interest/responsibility were significant predictors of job satisfaction. There was a significant difference between rural and urban nurses in terms of satisfaction from benefits and job security and the nurses' job satisfaction levels correlated negatively with their turnover intention. CONCLUSION: Several extrinsic and intrinsic factors are associated with nurses' job satisfaction in rural and urban settings. Developing strategies that improve satisfaction by modulating these specific factors may improve nurses' job satisfaction and reduce turnover. IMPACT: This study discussed how working in a rural or urban hospital may affect nurses' job satisfaction and turnover intention. The findings can help in improving nurses' job satisfaction and inform workforce planning to increase nurses' retention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".