Understanding Employee Absenteeism In Healthcare Sector

Factors affecting growth of a firm

There are a lot of emerging industries nowadays. The growth of all these different types of industries depends on a lot of different factors. One of the most important factors for the growth of a firm is employee absenteeism (Ward, 2016). The rise in employee absenteeism is supposed to have a negative impact on the business performance and the performance will fall (Folland, Goodman & Stano, 2016).

When a person is not present at a desired location in a proposed time, it is known as absenteeism. Considering this concept in the health care, absenteeism refers to the absence of medical staffs, especially nurses (Borkowski, 2015). Nurses help in taking care of the services received by the patients. In the absence of the nurses, these services will be hampered. Sufficient number of nurses and doctors must be present in the health care centres or hospitals to take care of the patients that come for treatments (Winwood, Stevens & Bowden, 2015).

Thus, it has already been identified that the problem of absenteeism is one of the major issues in the health care sector as the providing proper treatment to the patients is the most important factor. Thus the presence of ample number of doctors and nurses is very crucial in this sector as it deals with the health of people. Hence, evaluation of this issue is extremely important. Thus, in this research paper, the reasons behind this problem will be evaluated and discussed.  

The paper aims at finding reasons behind the employee absenteeism based on which the managers can adopt respective measures to overcome this problem. The objectives of the research can be given as follows:

• To compare the trend in absenteeism over time.
• To find the existence of variability in absenteeism within gender.
• To evaluate the causes behind absenteeism.

Realizing the importance of absenteeism in management practice several scholarly studies have already conducted. Scholarly research made by Durand et al., 2014 has considered absenteeism as an involuntary mechanism. Employees do not have controls over these most of factors resulting in absenteeism among the employees (Dai et al., 2015). Voluntary absenteeism occurs employees decides not to go office on their own. However, there may be situations where workers though willing to attend office but are unable go because of some unprecedented events (Shanks, 2016). From the organizations’ end, it is not possible to distinguish between voluntary and involuntary absenteeism.  Organization keeps a track on frequency of employee’s absenteeism and leave duration to differentiate between natures of absenteeism (Asay et al., 2016). In case, high frequency of absenteeism is obtained, then voluntary absenteeism is detected. With low frequency, absenteeism is likely to be involuntary (O’Donnell, Schultz & Yen, 2015). Organizations define voluntary absenteeism as sudden or unplanned leave and marks as short-term leave. For a health organization, various factors influence the tendency of absenteeism among health workers (Arbogast, 2016). The factors influencing absenteeism are classified in three main factors – workplace factors or content, personal factor, organizational or cultural factors.

Importance of employee absenteeism

Absenteeism may be resulted from variability in the employment sectors. The low resource availability is one factor causing absenteeism in public health care centers and public hospitals. Absenteeism might also depend on the size of the health care firm (Cascio, 2018). According to some researchers, a larger organization has minimum attachment within the employees and thus the performance of the employees remain unnoticed as the administration body is large and has to deal with a lot of other important factors (Gosselin, Lemyre & Corneil, 2013). It has been stated from an evaluation conducted by Kristman et al. (2016) that there is an increasing trend in the absenteeism of the number of employees over time. This increase is significantly high for large hospitals as compared to the smaller hospitals (Johnson et al., 2017). It has been observed from another analysis that the absenteeism of the employees in the hospitals of the main and the sub-districts in Kenya is extremely high whereas the employees have been quite regular in the dispensaries and health centres (Jones & Killion, 2017). The absenteeism can depend on the distance a person has to travel from their places to the hospitals or the health centres and dispensaries (Mercer et al., 2014). The picture in Nigeria is different than this theory developed. In Nigeria, travelling to the rural areas is a problem due to the absence of proper transport. Thus, considering the theory developed, absenteeism in the rural areas will be more than the urban areas. But it has been observed that the rural areas show more attendance than the urban areas (Mikami et al., 2017).

There can be various causes for a person to be absent. One of the major issues is the health issue. This is considered as a macro-economic factor (Sebastiano et al., 2017). This factor is helpful in reducing the societal cost and is helpful in allocating the resources effectively. If an employee calls in sick, much evaluation is not done on the issue as this becomes sensitive and personal (Zboril-Benson, 2016). The absence management managers are held responsible for managing the absenteeism in the organization (Doherty & Carino, 2015). Their work is to control the employees and make them come to work (Mudaly & Nkosi, 2015). For this, several strategies have to be developed by the managers that will encourage the employees to come to work (Hartman et al., 2016). On the other hand, the manager must also be well qualified to control the situation (Van Holland et al., 2015). The stakeholders must also be included in the policies that the management has instructed to. The position of the managers also has to be understood by them and hence several new practices has to be implemented to overcome this issue (Johnson et al., 2017). If there is a problem with the managers in understanding their work role, there will be rise of complications in the health sectors (Jourdain & Chênevert, 2015).

Reasons behind employee absenteeism

The work of the managers will be to make the employees understand the fact that they are valuable to the organization (Krane, 2016). This might reduce the absenteeism rate within the employees in the health care sectors. The employees who are sick and cannot come to office can be contacted by the managers on a regular basis and ask about their health and seek for their quick return (Kellner et al., 2016). At the time of difficulties, the managers might provide leaves to the employees (Shea et al., 2017). The manager might be a person to whom the employees can discuss their problems. This will reduce the problem of absenteeism (Kellner et al., 2016).

The methods with the help of which this research will be proceeded with will be highlighted in this section of the research paper. The method of collection of data, the type of data collected and the procedure with which the analysis will be undertaken will be described briefly in this section

The data collected for the purpose of this research is collected from a secondary source. The data on healthcare absenteeism has been derived from the ABS website and also secondary data has been collected from several research journals and research papers for the purpose of comparison with the results obtained from the quantitative analysis.

The data on the total number of people employed in the health care sector and the number of employees that did not work has been obtained for a period of 17 years. Considering these data comparison of trends of the absenteeism will be assessed and then from the results the reasons behind the variations can be identified. The difference in the male and female proportion of absent persons can also be tested using suitable statistical techniques of analysis. All the analysis will be conducted using the software Microsoft Excel.

Based on the research aims stated above, the following hypothesis can be framed:

Null Hypothesis (H₀₁): There is no significant difference between the actual and total absenteeism of total employees.

Alternate Hypothesis (H_A1): There are significant differences between the actual and total absenteeism of total employees.

Null Hypothesis (H₀₂): There is no significant difference between the actual and total absenteeism of male employees.

Alternate Hypothesis (H_A2): There are significant differences between the actual and total absenteeism of female employees.

Null Hypothesis (H₀₃): There is no significant difference between the actual and total absenteeism of female employees.

Methods for evaluation

Alternate Hypothesis (H_A3): There are significant differences between the actual and total absenteeism of female employees.

Null Hypothesis (H₀₄): There is no significant difference between the actual absenteeism of males and females.

Alternate Hypothesis (H_A4): There are significant differences between the actual absenteeism of males and females.

From April 2001 to December 2017, it can be said that the average number of people absent in that time frame is 863093. The standard deviation is 550197 which is quite high. This indicates that the number of people absent in each month on an average is not close to the mean number of people. There has not been a lot of variability in the number of people absent in the number of people absent in each of the months. Moreover, from the value of skewness, it can be seen that the number of people that are absent over the specified time is positively skewed. This indicates that the in most of the months, the number of people that are absent are less than the mean number of people. The results are shown in table 1.

Table 1: Number of people Absent
Mean	863.0932
Standard Error	38.80788
Median	690.4224
Mode	#N/A
Standard Deviation	550.1966
Sample Variance	302716.3
Kurtosis	7.968273
Skewness	2.729673
Range	3249.975
Minimum	418.472
Maximum	3668.447
Sum	173481.7
Count	201

Trend between actual and usual absenteeism is evaluated in in terms of total absenteeism, absenteeism among females and absenteeism among males.

The chart above shows a comparison of total absenteeism between those who are actually employed and those are predicted to be employed. The graph shows a more fluctuating trend for those who are actually as compared to those usually employed

For male and female absenteeism, the same trend is obtained as that of total employment. That is for actually employed males and females absenteeism trend is more fluctuating. This means employees in the health care organizations absent in an unpredictable manner.

The trend of absenteeism obtained graphically needs statistical confirmation to draw any conclusion. For this, first a correlation analysis is to be made

Table 2: Correlation table of absenteeism in total employment, males and females between actual and usual

	Total Number of People Absent (Actual)	Total Number of People Absent (Usual)	Total Number of Males Absent (Actual)	Total Number of Males Absent (Usual)	Total Number of Females Absent (Actual)	Total Number of Females Absent (Usual)
Total Number of People Absent (Actual)	1
Total Number of People Absent (Usual)	-0.019	1
Total Number of Males Absent (Actual)	0.991	-0.053	1
Total Number of Males Absent (Usual)	0.088	0.881	0.037	1
Total Number of Females Absent (Actual)	0.989	0.018	0.961	0.140	1
Total Number of Females Absent (Usual)	-0.128	0.862	-0.135	0.520	-0.118	1

The correlation between usually and actually employed persons of absenteeism is -0.019. This means as actual absenteeism increases the predicted or usual absenteeism declines. For male absenteeism, the correlation between actual and usual employment is 0.0373. The positive value of correlation coefficient implies increase in actual male absenteeism increases the usual absenteeism. In case of female absenteeism, the correlation coefficient is -0.1175. This again implies an opposite relation between actual and usual female absenteeism.

At first regression has been conducted to establish the correctness of the prediction of the actual absenteeism observed from the usual absenteeism. It can be seen from the value of the R Square that usual absenteeism is an extremely bad predictor of the actual absenteeism as it can explain only 0.03% of the variability in the actual absenteeism. The significance value from the ANOVA table is also more than the level of significance (0.05). Thus, the model so developed is also insignificant. The regression tables are attached in the appendix section.

Hypothesis

T test is used to examine statistical relation. The test results for testing the proposed hypotheses are given below

Table 3: t-Test: Paired Two Sample for Means (Total)
	Percentage Change total persons (actual)	Percentage Change total persons (usual)
Mean	26.94006736	2.973469382
Variance	10228.30999	639.0765526
Observations	201	201
Pearson Correlation	-0.021375041
Hypothesized Mean Difference	0
df	200
t Stat	3.243163268
P(T<=t) one-tail	0.000692608
t Critical one-tail	1.652508101
P(T<=t) two-tail	0.001385215
t Critical two-tail	1.971896224

From the obtained result, it is seen that critical t value is less than the computed t value. Therefore, the null hypothesis of no significance difference in mean of actual and usual absenteeism is rejected. More specifically, the mean of actual absenteeism is greater than usual absenteeism.

Table 4: t-Test: Paired Two Sample for Means (Females)
	Percentage Change Females	Percentage Change Females
Mean	27.881786	3.537139853
Variance	9387.736131	806.5603075
Observations	201	201
Pearson Correlation	-0.058111181
Hypothesized Mean Difference	0
df	200
t Stat	3.366009702
P(T<=t) one-tail	0.000457201
t Critical one-tail	1.652508101
P(T<=t) two-tail	0.000914401
t Critical two-tail	1.971896224

As obtained from the test result, the p value for two tale test statistics is 0.0009, which is lower than significance level of 0.05. Therefore, the null hypothesis of no significance difference of female absenteeism between actual and usual statistics is rejected.

Table 5: t-Test: Paired Two Sample for Means (Males)
	Percentage Change Males	Percentage Change Males
Mean	26.48112749	5.772347021
Variance	11556.27055	1296.429531
Observations	201	201
Pearson Correlation	-0.02467553
Hypothesized Mean Difference	0
df	200
t Stat	2.570702446
P(T<=t) one-tail	0.005437936
t Critical one-tail	1.652508101
P(T<=t) two-tail	0.010875872
t Critical two-tail	1.971896224

For males’ absenteeism, the obtained t test statistics is greater than the critical t value of both tale test. This implies rejection of null hypothesis of no significance difference between mean male absenteeism between usual and actual employees.

Table 6: t-Test: Paired Two Sample for Means (across genders)
	Percentage Change Males	Percentage Change Females
Mean	26.48112749	3.537139853
Variance	11556.27055	806.5603075
Observations	201	201
Pearson Correlation	-0.138766565
Hypothesized Mean Difference	0
df	200
t Stat	2.830174487
P(T<=t) one-tail	0.002563477
t Critical one-tail	1.652508101
P(T<=t) two-tail	0.005126955
t Critical two-tail	1.971896224

The above results shows variation of absenteeism across genders. The p value is 0.002. As the p value is less than the significance level, the null hypothesis of no significant difference between male and female absenteeism is rejected.

The results shows a volatile absenteeism trend in health care organizations. In reality, the health centers employees tend to absent more than that usually happened to be. However, no increasing trend for absenteeism is obtained from the results. This contradicts prior studies claiming an overtime-increasing trend in absenteeism (Engel et al., 2014). Significant difference in mean absenteeism between actual and usually employed staffs exist not only in terms of total employment but also in female and male employment.

From the analysis of this hypothesis, it has been observed that there are significant differences between the actual and the usual data obtained on absenteeism on the total number of employees in the health care sector.

From the analysis of this hypothesis, it has been observed that there are significant differences between the actual and the usual data obtained on absenteeism on the total number of male employees in the health care sector.

From the analysis of this hypothesis, it has been observed that there are significant differences between the actual and the usual data obtained on absenteeism on the total number of female employees in the health care sector.

The results confirm that the tendency of absenteeism differs significantly between male and female employees. Males are likely to be more absent than female employees.

There are different factors that causes absenteeism among health center workers.  Sickness of the workers is one factor leading to involuntary absenteeism. On an average, nearly 5% of worker fell sick for a given day. Sometimes organizations do not record absenteeism properly. The marital status of female employees is an important factor that can contribute to absenteeism among female workers. Age, job position, size of organization and structure of management are some other factors influencing absenteeism.

Data analysis and interpretation

Conclusion, Limitations and Recommendations

Conclusions

As far as the discussion has been made, it can be seen that significant difference exists in the trend absenteeism between actual and usual employment. This means the usual absenteeism statistics cannot capture the true picture of absenteeism in health organizations because the trend shows significant difference in their patterns. 

Absenteeism also differs between males and female employees. The male absenteeism is likely to be higher than female absenteeism. In total employment, an increase in actual absenteeism causes a decline usual absenteeism. For male absenteeism, the relation between actual and usual absenteeism is negative and for females, the relation is negative.

Finally, factors leading to absenteeism among health workers include age, marital status, health condition, job position, organization structure and others.

This research has been conducted in a very short span of time. Due this time constraint, the collection of data has been limited. Data on other attributes such as marital status, age of the employees, hierarchical level and personal health conditions could be other important factors for absenteeism.

Further study can be conducted by collecting data on the factors such as marital status, age of the employees, hierarchical level and personal health conditions. The effects of these factors on absenteeism of the employees can be studied. Moreover, the factors that has been used to predict the usual trend of the absenteeism of the employees also have to be assessed and developed. The usual trend has given a very bad prediction of the actual trend. Thus, to make a better prediction the factors that has been used to predict the usual trend of absenteeism needs to be developed.

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