An organization is a group of people working together towards a common goal. So the meaning of the term organization itself underlines the importance of teamwork. The dictionary defines teamwork as a "Cooperative effort on the part of a group of persons working together in the interests of a common cause.” It means working in unison for a higher purpose or reward, than what they themselves will receive, in other words people working together for an unselfish purpose. Teamwork is an essential part of workplace success. When people work together to accomplish a goal, everyone benefits. The ability to work as part of a team is one of the most important skills in today’s world.

Teamwork involves building relationships and working with other people. (Delarue et al. 2003) has defined teams as groups of employees who have at least some collective tasks and where the team members are authorized to regulate mutually the execution of these collective tasks. Teamwork replies upon individuals working together in a cooperative environment to achieve common team goals through sharing knowledge and skills. The importance of team working in health care has been emphasized in numerous reports and policy documents on the National Health Service. It particularly emphasized the importance of team working if health and social care for people are to be of the highest quality and efficiency. The best and most cost-effective outcomes for patients and clients are achieved when professionals work together, learn together, engage in clinical audit of outcomes together, and generate innovation to ensure progress in practice and service. Studies have proven that health care professionals working in teams have much lower levels of stress than those working in looser groupings or working alone. Hence, the present paper is an attempt to assess the perception of doctors regarding the existence of teamwork at government hospitals as well as the factors impacting the same.

2. REVIEW OF LITERATURE:

Little empirical evidence is there on the relationship between staff’s perceptions of the quality of teamwork and the perceived quality as well as safety of patient care. For example, studies about clinicians’ attitudes towards safety-relevant behaviors as barometers of safety found that although attitudes to these behaviors were generally positive, staffs’ responses also indicated a belief in personal invulnerability to stress and fatigue (Flin et al. 2003). It was found that surgical errors increased considerably with greater disruptions and that teamwork and communication problems were the strongest causes of surgical errors (Wiegmann at al. 2007).

Surveys and interview studies concerning attitudes toward teamwork indicate that – consistent with many other high-risk industries – healthcare providers attribute a high level of importance to teamwork aspects such as communication or coordination (Ummenhofer et al. 2001). Studies using objective measures of the quality and safety of patient care also indicate a link with teamwork (Davenport et al. 2007).

Observational studies of teamwork have identified patterns of communication, coordination, and leadership that support effective teamwork. However, only a few studies could ascertain a direct link between specific teamwork behaviors and clinical performance or patient outcome (Kohn et al. 2002). Coordination is indispensable to teamwork as different team members consistently perform multiple interdependent tasks concurrently. Ethnographic research in anesthesia has highlighted that teams coordinate not only through verbal communication but also through their work environment (Hindmarsh and Pilnick, 2002). The above literature clearly brings out the significance of teamwork in effective patient care delivery.

3. SIGNIFICANCE OF THE STUDY:

For any organization to achieve its objectives, it needs a set of people who work as a real team. Health care is an arena where teamwork assumes even more significance. The satisfaction of the patients depends on the effective and prompt delivery of care which in turn depends on the extent of joint effort that the doctors, nurses and support staff put in. As such, it is very important to ensure that a positive environment that supports team work is sustained at hospitals. The case of government hospitals, on which a major chunk of the public is dependent for their health care needs, is not different from this. Hence, the present study tries to understand the perceptions of doctors regarding extent of team work existing at government hospitals in Kerala.

4. METHODOLOGY:

The researcher followed a descriptive approach in conducting the study. Data were collected from doctors at various districts and general hospitals across Kerala. A structured questionnaire was administered among a sample of 240 doctors identified at the convenience of the researcher from various government district hospitals across Kerala .The questionnaire was framed in such a way as to elicit the opinion of respondents on various aspects relating to teamwork at hospitals like the extent of co-operation between doctors, nurses and support staff, extent of co-operation among nurses, sense of unity among staff members etc. The collected data was then analyzed using factor analysis. Factor analysis tries to bring inter-correlated variables together under more general, underlying variables. More specifically, the goal of factor analysis is to reduce “the dimensionality of the original space and to give an interpretation to the new space, spanned by a lower number of new dimensions which are supposed to underlie the old ones” (Andy, 2000) or to explain the variance in the observed variables in terms of underlying latent factors”. In the present paper, factor analysis was done to identify the key variables impacting the effectiveness of teamwork at government hospitals and to group them into certain factors based on common properties. The factor scores thus obtained were then subjected to multiple regression analysis. Multiple regression is a statistical technique that allows us to predict the value of one variable on the basis of values of several other variables. There will be two set of variables – predictor variables which are helpful in predicting the values of other variables and the criterion variables for which the values are predicted based on the values of predictor variables.

This statistical technique can be used while exploring linear relationships between the predictor and criterion variables. Multiple regression analysis helps us to understand the significance level of different dependent variables in relation to one or more independent variables and also to identify the most significant factor(s) (Brace et al. 2009). In this paper regression analysis was performed to find out whether there existed significant difference in the perception of male and female doctors regarding the existence of effective teamwork at government hospitals in Kerala. SPSS version 16 was used to analyze the data.

5. RESULTS AND DISCUSSIONS:

Table. 1 KMO and Bartlett's test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.878
Bartlett's Test of Sphericity	Approx. Chi-Square	1.012E3
	Df	21
	Sig.	.000

Source: Survey Data

The KMO value varies between 0 and 1. A value of 0 indicates that factor analysis is inappropriate for the data and a value of 1 indicates that factor analysis will yield distinct and reliable results. A value of 0.5 or above means that the sample is adequate and we can proceed with factor analysis whereas if it is below 0.5 we have to collect more data (Andy, 2000). As seen from Table 1 the KMO value for our data is 0.878 which means data is adequate and we can go ahead with factor analysis. For factor analysis to work there has to be some kind of relationship between the variables and this is tested using the Bartlett’s Test of sphericity. This test indicates whether factor analysis is appropriate for a given set of data. Factor analysis can be considered appropriate for a data only if the significance value is less than 0.05 (Andy, 2000). As the significance value for the present data as shown in Table 1 is 0.000, factor analysis is appropriate for this data. As the present data set satisfies both KMO test and Bartlett’s test, factor analysis is appropriate.

Table. 2 Communalities

Variables	Initial	Extraction
Existence of good co-ordination among doctors, nurses and support staff.	1.000	.701
Extent of help and support received from nurses in patient care.	1.000	.692
Existence of good co-operation among nurses.	1.000	.644
Extent of help and support received from support staff in patient care.	1.000	.612
Existence of co-operation between nurses and support staff.	1.000	.701
Extent to which doctors, nurses and support staff jointly take up the responsibility of patient care.	1.000	.606
Existence of sense of oneness among all staff members.	1.000	.506

Extraction Method: Principal Component Analysis Source: Survey Data

Table 2 explains the communalities before and after extraction. Principal component analysis works on the assumption that all variance is common. So before extraction all communalities are 1. Column two, i.e., the extraction column indicates the percent of common variance associated with each question. Hence from Table 5.68, we can find that 70.1 percent of variance associated with the variable ‘Existence of good co-ordination among doctors, nurses and support staff ’ is common, 69.2 percent of variance associated with the variable ‘Extent of help and support received from nurses in patient care’ is common and so on. The table clearly shows the percent of common variance associated with each variable. The highest percent of common variance is in the case of ‘Existence of good co-ordination among doctors, nurses and support staff’ as well as ‘Existence of co-operation between nurses and support staff’ and lowest is in the case of ‘Existence of sense of oneness among all staff members’.

Table 3 explains the eigenvalues before and after extraction. Before extraction there are 7 eigenvalues as there were 7 variables included in the analysis. The eigenvalues associated with each factor shows the variance associated with each factor. It also shows eigenvalues in terms of percent of variance. For e.g. the first factor, i.e., ‘Existence of good co-ordination among doctors, nurses and support staff’ explains 63.72 percent of variance; second factor explains 12.18 percent of variance and so on. It is clear from Table 5.69 that the first few factors explains relatively larger amount of variance when compared to the later ones. The second part of the table labeled as ‘Extraction Sum of Squared Loadings’ shows those factors with eigenvalues greater than 1. From Table 3 it is clear that only one factor was extracted. So all the variables included in the analysis namely ‘Existence of good co-ordination among doctors, nurses and support staff’, ‘Extent of help and support received from nurses in patient care’, ‘Existence of good co-operation among nurses’, ‘Extent of help and support received from support staff in patient care’, ‘Existence of co-operation between nurses and support staff’, ‘Extent to which doctors, nurses and support staff jointly take up the responsibility of patient care’, and ‘Existence of sense of oneness among all staff members’ loaded across a single factor ‘Joint effort in patient care’.

Table 3 Total variance explained

Components	Initial Eigenvalues			Extraction Sums of Squared Loadings
Components	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.461	63.727	63.727	4.461	63.727	63.727
2	.853	12.187	75.914
3	.459	6.563	82.477
4	.395	5.638	88.114
5	.358	5.113	93.227
6	.263	3.751	96.978
7	.212	3.022	100.000

Extraction Method: Principal Component Analysis. Source: Survey Data

From regression results (Table. 4) it was concluded that the factor that emerged after principal component analysis was found to be insignificant as far as gender of respondents was considered. Hence it was concluded there was no significant difference in the opinion of male and female doctors regarding the existence of joint effort in patient care at government hospitals. H1 was thus accepted. It was further concluded that effective teamwork in patient care hardly existed at government hospitals.

6. CONCLUSION AND LIMITATIONS OF THE STUDY:

From the above discussions we can conclude that various factors like „Existence of good co-ordination among doctors, nurses and support staff‟, “Extent of help and support received from nurses in patient care,” “Existence of good co-operation among nurses,” “Extent of help and support received from support staff in patient care,” “Existence of co-operation between nurses and support staff‟, “Extent to which doctors, nurses and support staff jointly take up the responsibility of patient care‟, and “Existence of sense of oneness among all staff members‟ were important in ensuring teamwork or joint effort it patient care, both male and female doctors felt that it hardly existed at government hospitals in Kerala. Hence, the authorities concerned must consider this seriously and make all possible efforts to induce team spirit among employees which is very essential for effective patient care delivery. However as the conclusions are solely based on analysis of inputs provided by the surveyed doctors, there is always a chance of bias in the response provided which may get reflected in the findings as well. Moreover, the findings of the study should be generalized cautiously. However, there is always a scope to extent the study to Private sector, co-operative sector etc. as well as to other states.

7. REFERENCES:

1. Andy P. Field. (2000). Discovering Statistics Using SPSS for Windows: Advanced Techniques for the Beginner. SAGE Publications.

2. Brace, N., Kemp, R., and Snelgar, R. (2009). SPSS for Psychologists. Hampshire: Palgrave Macmillan.

3. Davenport, D. L., Henderson, W. G., Mosca, C. L., Khuri, S. F., and Mentzer, R. M. (2007). Risk-adjusted morbidity in teaching hospitals correlates with reported levels of communication and collaboration on surgical teams but not with scale measures of teamwork climate, safety climate, or working conditions. Journal of the American College of Surgeons, 205(6), 778-784.

4. Delarue, A., Gryp, S., and Van Hootegem, G. (2003). Productivity outcomes of teamwork as an effect of team structure. In 7th International Workshop on Teamworking. Prato, Italy.

5. Flin, R., Fletcher, G., McGeorge, P., Sutherland, A., and Patey, R. (2003). Anaesthetists' attitudes to teamwork and safety. Anaesthesia, 58(3), 233-242.

6. Hindmarsh, J., and Pilnick, A. (2002). The tacit order of teamwork: collaboration and embodied conduct in anesthesia. The Sociological Quarterly, 43(2), 139-164.

7. Kohn, L. T., Corrigan, J. M., and Donaldson, M. S. (2002). To err is human: building a safer health system. National Academy of Science, Institute of Medicine.

8. Ummenhofer, W., Amsler, F., Sutter, P. M., Martina, B., Martin, J., and Scheidegger, D. (2001). Team performance in the emergency room: assessment of inter-disciplinary attitudes. Resuscitation, 49(1), 39-46.

9. Wiegmann, D. A., ElBardissi, A. W., Dearani, J. A., Daly, R. C., and Sundt, T. M. (2007). Disruptions in surgical flow and their relationship to surgical errors: an exploratory investigation. Surgery, 142(5), 658-665.

Received on 11.10.2015 Modified on 04.11.2015

Asian J. Management; 6(4): Oct. -Dec., 2015 page 321-324

DOI: 10.5958/2321-5763.2015.00047.5

Model		Unstandardised Coefficients		Standardised Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	1.446	.032		44.884	.000
1	Joint effort in patient care	-.010	.032	-.021	-.322	.748