Job Demands, Resources, and Nurse Work Engagement Study

Study Overview and Research Design

This paper critically reviews a study by Hala Gabr and Ahlam Mahmoud El-Shaer published in Public Policy and Administration Research. The purpose of the study was to determine whether relationships exist between the job demands and resources of head nurses and their work engagement. The two authors argue that understanding the role of the head nurse is essential to understanding their sense of engagement at work. The researchers employed a correlational design and conducted their study in the general units of four university hospitals in Mansoura, Egypt: Emergency Hospital, Main University Hospital, Specialized Medical Hospital, and Pediatric Hospital. These hospitals were selected because they provide a wide range of services and could yield in-depth information about the work engagement of head nurses. The dependent variable was work engagement, while the independent variables were job demands and job resources (Gabr & El-Shaer, 2013).

Study Participants and Data Collection

The study included all head nurses working in the general units of the four hospitals listed above. The Main University Hospital had 66 head nurses, followed by 59 in the Emergency Hospital, 34 in the Specialized Medical Hospital, and 20 in the Pediatric Hospital. To be included, head nurses had to have at least one year of experience as a first-line nurse manager and had to be available during the data collection period (Gabr & El-Shaer, 2013).

The study used three tools to collect data. The first was the Job Demands Scale, developed by the researchers based on their review of the literature. This tool collected demographic information about participants and asked 32 questions relating to the job demands of head nurses in the workplace. Demands were categorized as physical, social, psychological, or organizational (Gabr & El-Shaer, 2013).

The second tool was the Job Resources Scale, which identified the job resources of head nurses that facilitate achievement of work goals, reduce workplace demands, and stimulate personal growth. This tool included six subscales covering resources such as autonomy, organizational support, and performance feedback (Gabr & El-Shaer, 2013).

The third tool was the Work Engagement Scale, which measures the work engagement of head nurses through 17 items categorized under three dimensions: vigor — defined as persistence in the face of challenges; dedication — defined as a sense of significance or enthusiasm derived from one's work; and absorption — defined as being totally immersed in one's work (Gabr & El-Shaer, 2013).

Using coefficient alpha, the researchers found that the internal consistency reliability of the work engagement scale was 0.90. To verify the clarity, feasibility, and focus of the study, the researchers conducted a pilot study on 15 head nurses at the Main University Hospital in Mansoura. Following interviews, the head nurses were given a questionnaire, with each sheet estimated to take between 10 and 15 minutes to complete (Gabr & El-Shaer, 2013).

Statistical Methods and Analysis

After collecting the data, the researchers used simple means and standard deviations to summarize the numerical variables. They conducted multiple regression analysis to evaluate whether job resources and job demands would be predictive factors of work engagement, and used standard linear regression to test their hypothesis. The r-test was used for correlation analysis between numerical variables (Gabr & El-Shaer, 2013).

The use of correlation in research has a long history. Its primary advantage is that it allows researchers to predict one set of variables from another (Voss & Parasuraman, 2003). For example, knowing students' SAT scores allows researchers to predict their academic achievement in college. In this study, since correlation was used to predict work engagement from job resources and job demands, the researchers were interested in determining whether a relationship existed between these variables and how it could be quantified on a particular scale.

At the same time, correlation has one significant weakness: it does not indicate causation. Tables 4 and 5 of the study show significant correlations between components of job demands and job resources and work engagement. However, this correlation does not mean that job demands or job resources cause work engagement. This is a common misconception and should be avoided when interpreting the results of this study. A positive correlation suggests that when the independent variable increases or decreases, the dependent variable follows the same pattern. A negative correlation suggests the opposite pattern.

The multiple regression model was chosen to determine the relationship between the dependent and independent variables. Since the dependent variable is work engagement and the independent variables are job demands and job resources, the researchers used multiple regression to estimate this relationship. However, multiple regression does not verify whether the data are linear in nature — it assumes a linear relationship between the dependent and independent variables. A second assumption is the absence of multicollinearity, meaning that the independent variables — job demands and job resources — are not tested against each other.

The significance level was set at 0.05, the most common threshold in quantitative research. As the pioneer of significance testing, Fisher argued that p < .05 was the best determinant of a positive experimental outcome. This level suggests a one-in-forty chance that the response could be at the upper or lower limit of the distribution (Peck & Devore, 2011). This spread is a reasonable generalization of study results, and the use of p < .05 by the researchers is therefore justified.

Conclusion

Overall, the study was successful. The findings supported the central hypothesis and demonstrated significant relationships between the independent variables — job demands and job resources — and the dependent variable, work engagement. The research is credible in that its discussion situates findings within the context of similar studies conducted by other researchers. However, from a critical statistical standpoint, the researchers failed to address one essential analytical component — the role of demographic variables — and this omission somewhat diminishes the value of the study's findings.

References

Gabr, H., & El-Shaer, A. M. (2013). Head nurses' job demands and resources and its relationship with their work engagement at Mansoura University Hospitals. Public Policy and Administration Research, 3(4), 33–42.

Peck, R., & Devore, J. (2011). Statistics: The exploration & analysis of data. Cengage Learning.

Voss, G. B., & Parasuraman, A. (2003). Conducting measurement validation with experimental data: Cautions and recommendations. Marketing Letters, 14(1), 59–73.