Exploring Job Satisfaction and Caring Behaviors among Critical Care Nurses: A Descriptive Analytic Study
Bibliographic record
Abstract
Introduction: Caring behaviors are actions that prioritize the welfare of patients. Job satisfaction is one factor that can influence nurses' caring behaviors, as nurses with higher job satisfaction tend to exhibit more positive caring behaviors.Objective: The study aimed to assess critical care nurses' job satisfaction levels, explore the dimensions of their caring behaviors, investigate the relationship between their caring behaviors and job satisfaction, and identify the significant independent predictors of nurses' job satisfaction and caring behavior in critical care units.Method: The study used a descriptive-analytic, correlational, cross-sectional research design. It was conducted at the critical care units of King Fahd Military Medical Complex in Saudi Arabia. A convenience sample of 112 registered nurses completed an online survey that included the Job Satisfaction Survey and the Caring Behavior Inventory.Results: The majority of respondents fall under the category of Ambivalent job satisfaction, constituting 67.9% of the total nurses. The analysis indicated a moderate job satisfaction level and a high caring behavior among critical care nurses. The study found a positive correlation between nurses' job satisfaction and caring behaviors. Factors such as working hours, work unit, and years of experience were significantly associated with nurses' job satisfaction. However, factors like operational procedures, co-worker relationships, nature of work, and working hours were significant predictors of nurses' caring behavior.Conclusion: The study highlights the importance of addressing job satisfaction factors to enhance critical care nurses' caring behaviors, which can ultimately improve patient outcomes. Strategies to enhance nurses' job satisfaction and support their caring practices should be a priority for healthcare organizations
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".