Job demands, resources, and work engagement in head nurses

¶ … Hala Gabr and Ahlam Mahmoud El-Shaer in Public Policy and Administration Research. The purpose of the study was to determine if there are relationships between the job demands and resources of head nurses with their work engagement. The two authors argue that it is important to understand the role of the head nurse in order to understand their sense of engagement in their work. The researchers used a correlation design. They conducted their study in the general units of four University hospitals in Mansoura -- Emergency Hospital, Main University Hospital, Specialized Medical Hospital, and Pediatric Hospital. These hospitals were chosen because they provide a wide range of services and would provide 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

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

Data collection

Using coefficient alpha, the researchers found that the internal consistency reliability of the work engagement scale was 0.90. To ascertain 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. After participating in interviews, the head nurses were given the questionnaire to respond to the questions. Each sheet was estimated to take between 10 and 15 minutes per nurse to answer Gabr & El-Shaer, 2013()

Data analysis and results

After collecting the data, the researchers used simple mean and standard deviation 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. To test their hypothesis the researchers used standard linear regression. They used the r-test for correlation analysis between numerical variables Gabr & El-Shaer, 2013()

The use of correlation in studies dates back many years. The largest advantage of this method according to scholars is that it allows the researchers to predict which one set of variables Voss & Parasuraman, 2003.

A good example is if we know the SAT scores of students, we can predict their achievement in college. Therefore, for this study, since they used correlation to predict work engagement using job resources and job demands, the researchers were interested to know whether there was a relationship between these variables and how this can be quantified on a particular scale.

At the same time, the correlation method has one significant weakness. This is that it does not indicate causation. In the results section of the paper, table 4 and 5 show that there is significant correlation between components of job demands and job resources to work engagement. However, this correlation does not mean that job demands or job resources cause work engagement. This is a common misconception and should also be avoided in interpreting the results of this study. When correlation is positive, it suggests that when the independent variable increases or decreases, the other follows the same patter. A negative correlation suggests that as the independent variable increases, the other follow the opposite pattern.

The multiple regression model method chosen the researchers is used to determine the relationship between dependent and independent variables. Since the dependent variable of this study is work engagement and the independent variables are job demands and job resources, the researchers use the multiple regression method to estimate this relationship. Multiple regressions, however, does not check whether the data is linear in nature. It assumes that the relationship between the dependent and independent variables is linear. The second assumption is that there is no multi-collinearity. This means the independent variables -- job demands and job resources - are not tested against each other.

The significance level was 0.05. This level of significance is the most common in quantitative studies. This is because the pioneer of significance testing, Fisher, argued that the P

This spread is a good generalization of the study result. Therefore, the use of the P

Demographic characteristics of the study participants were reported as simple percentages of the total in table 1 of the research findings. Overall, the findings show that about 1 in three participants were working in the Emergency Hospital and 2 in three of all head nurses were between the age of 25 and 30 years. About half of the head nurses had less than 10 years of experience while about 90% were married Gabr & El-Shaer, 2013.

The advantage of demographic data is that it allows the researcher to determine other factors that affect the relationship between the independent and dependent variable. However, the researchers did not evaluate the significance of these demographic factors in their regression model. This is a limitation of the study since they may have found that factors such as age, marital status, years of experience, or the hospital where the head nurse is working also determines their work engagement.

The researchers also summarized the descriptive statistics of job demands and job resources as the head nurses perceived in table 2. The mean score of the total job demands of the head nurses was 109.35 representing more than two-thirds of the maximum score. The highest score was borne by the workload of the head nurse with a mean score that was more than 75% of the maximum. In terms of job resources, overall, the mean score of the head nurses was 135.22 representing more than 70% of the maximum score Gabr & El-Shaer, 2013.

This method of presenting descriptive statistics shows the level of detail that the researchers went to in presenting their data. In descriptive statistics, it is important to analyze the data in as much detail as possible in order to present a quick picture of the study findings.

Table 4 of the study findings suggests that as job demands in all three levels - vigor, dedication, and absorption -- are increasing, work engagement increases, though at different rates. Overall, when job demands increased, the total work engagement also increased with a correlation coefficient of 0.198 with a statistical significance of 0.001 Gabr & El-Shaer, 2013.

Since the p-value is less than 0.05, which is the set significance level of the study, we can be confidence in the significance of the result. However, the result only shows moderate correlation between components of job demands and work engagement.

Table 5 of the results section also shows a moderate but significant correlation between the total components of work engagement and job resources with a correlation coefficient of 0.469 with a p-value of

Assuming that the core assumptions in multiple linear regression were upheld, we can be 95% confident in the results since the p-value is less than the set level of significance (0.05).

Limitations

The major limitation of the study that is identified from this review of the study's statistical methods is the lack of analysis of the effect of demographic factors on the overall result of the study. Some demographic factors such as age, hospital, marital status, and qualification that had a less normal spread of the population should have been investigated further to find out whether they could influence the relationship between the independent and dependent variable. The lack of this analysis somewhat reduces the credibility of the findings of this research paper since it means a critical component that should have been evaluated was left out by the researchers.

Conclusion

Overall, the study was successful. The study findings supported the hypothesis of the study and showed significant relationships between the independent variable -- job demands and job resources -- and the dependent variable -- work engagement. This research is credible since its discussion shows how it compared to similar studies conducted by other researchers though from a critical statistics point-of-view, there is an essential analysis component that the researches failed to focus on and that somewhat diminishes the value of the findings of the study.