How are the temporary workers? Quality of life and burn-out in a call center temporary employment in Italy: a pilot observational study



Alice Mannocci; Alessio Natali; Vittoria Colamesta; Antonio Boccia; Giuseppe La Torre

Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza, Università di Roma, Rome, Italy

Address for correspondence




OBJECTIVES. The aim of this study was to quantify the level of health related quality of life and burnout in a call centers sample of precarious workers.
METHODS. An observational study was carried out in Italy. A self-reported anonymous questionnaire was administered to temporary workers in order to estimate burn-out and quality of life levels.
RESULTS. 227 questionnaires were collected. 78% of the sample was female and the mean age was 35.48 years (SD = 9.91). Among the participants, 40% were smokers, 42% regularly drinkers, 65% changed the body weight (more than 5 kg, in 1 year).
The mental component score (MCS) was significantly better in subject that have a regularly life style, for example in those no change their body weight (p = 0.001), sleep more than 7 hours (p = 0.018) and followed a diet (p = 0.035). The DP (depersonalization) is significant higher in former smokers (p = 0.031), in underweight subjects (p = 0.025) and in the group that have a precarious employment of over 2 years (p = 0.013).
CONCLUSIONS. This investigation shows that in this particular category of atypical workers depressive symptoms and quality of life were lower than the general population. It is important to underline that the interpretation of the results is limited partly by the observational character of the study.

Key words: quality of life. temporary employment. call centers. atypical workers. burnout




Call centers represent an important working market in several countries. Often, people that work in call centers societies have a permanent precarious working condition. Data relating to the Italian employment, collected in the second quarter of 2012, show that precarious condition affects almost 3 million [1] short and not extended contracts lead inevitably to an increase in the unemployment rate.

Temporary working is an employment form almost widespread in the world with globalization process; work position is occupied only for a well certain period of time and then, according to business employee can keep on the same conditions, come out working relationship or be hired in permanent way. Virtanen et al. [2] in a review showed a higher risk of psychological morbidity and occupational injury in temporary workers compared to permanent ones, due to job insecurity and absence of security at the workplaces, respectively. Precarious workers present particular features compared to permanent employed: low social level, low economic status, not sentimentally engaged, a smaller number of children [3]. Furthermore, workers employed in occasional and seasonal jobs showed a significant dissatisfaction [4, 5].

Generally speaking, call centers activities include inbound or outbound services. The first ones administer incoming product support or information inquiries from consumers, whilst the second type is operated for telemarketing, solicitation of charitable or political donations and debt collection. Ferreira and Saldiva [6] indicated that call centers working on outbound services faced higher productivity pressures and conflicts associated with supervisors than on inbound services, but work related to inbound services was more repetitive, and lacked autonomy and a structured work/rest schedule. Lin et al. [7] found a correlation between high job stress and risks of multiple health problems. Several situations like job stress, competition, relationships with customers, a limited autonomy cause a stressful condition and further, movements represent risk factors for musculoskeletal symptoms [8]. Concerning psychological risk factors, an increased workload was strongly associated with greater stressful, likewise, there is an important correlation between mental health problems and call center employees compared to groups in other jobs [9]. Moreover, several studies regard specifically the relationship between shift work and physical health effects in short and long term [10-13]. Particularly, one study [14] has demonstrated that inappropriate sleep was associated with negative physical health and for this reason, it is very important to promote sleep hygiene, which may decrease overall morbidity in call centers workers.

In the light of the findings it is important to consider the potential health implications of job stress from interpersonal factors that are unique in a call center setting.

The aim of study was:

a) to quantify the level of health-related quality of life and burnout in a call centers sample of precarious workers;

b) to assess factors potentially associated to these outcomes.



Study design and settings

The observational study was performed following the STROBE statement [15].

This investigation was carried out in two call centers in Rome, Italy, from November 2011 to January 2012. The information was collected using a questionnaire directly administered at workplace. Those who decided to participate were requested to go in the administrative office to explain the scope of the research and to give the questionnaire. One center employed either inbound operators or outbound operators, the other center had only outbound workers. Consensus document concerning the participation to the study was obtained from each participant.

Data collection procedures and tool

All participants have been recruited on voluntary basis asking them to fill in the questionnaire during working break in order not to hinder their work. The study was conducted according to the Helsinki declaration and the protocol of the study was approved by the local ethic committee.

Questionnaires were nameless and self-reported, and returned directly to the researcher. The following information were obtained:

- socio-demographic data: gender, age (< 36; ≥ 36 years), civil status, quality relationship with partner (very good, good, sufficient, insufficient, low), number of sons, city of residence, distance between house and work, home ownership (yes/no), loan (yes/no), year of the loan;

- background and activites: university degree (yes/ no), years after graduation, type of contract, years working (≤ 2; > 2);

- anthropometric and lifestyle characteristics: BMI, changes of the body weight (more or less than 5 kg in one year), special diet (for example: vegetarian, glutenfree, low-sodium, etc.) (yes/no), smoking (yes/no), hours of sleep (≤ 7; > 7) and alcohol (yes/no);

- quality of life with using SF-12, and computing physical component score (PCS) and mental component score (MCS) [16];

- stressfull/burnout using Maslach's questionnaire [17] and computing emotional exhaustion (EE), personal accomplishment (PA) and depersonalization (DP).

Statistical analysis

Descriptive statistics (median, interquartile range, means, and standard deviation [SD]) was reported for all quantitative variables; percentages and frequencies were generated for qualitative variables.

Univariate analysis was conducted to evaluate possible associations between outcomes (EE, DP, PA, PCS, MCS) and socio-demographic variables, lifestyle and anthropometric characteristics.

Parametric or no parametric approach were established using Kolmogorov Smirnov normality test. Tstudent and Mann-Whitney tests were applied to compare two independent samples. Leven's test was used to assume or non equal variances. Kruskal-Wallis and ANOVA tests were used to compare multiple groups.

Five linear multivariate regression models were conducted using the following dependent variables: PCS, MCS, EE, DP and PA. The covariates included in these models were those that at the univariate analysis had obtained a p-value < 0.25 in according to Hosmer and Lemeshow's procedures. The fit of the model was estimated by the correlation coefficient R2.

The correlation analysis between outcomes was carried out using Pearson's coefficient.

The level of significance was set at p < 0.05.

Data analysis was conducted using IBM software Statistical Package for Social Sciences (SPSS) version 19.0 for Windows (SPSS Inc. Chicago, Illinois, USA).



Out of 265 questionnaires distributed, 227 were returned filled in (response rate of 85.7%). The remaining 38 questionnaires belonged to workers that decided not to take part or had filled them incompletely: the noresponders had same distribution for gender and age of responders.

Table 1 shows the socio-demographic characteristics of the participants: female accounted for 78% of the total sample that had a mean age of 35.48 years (SD = 9.91). In Table 1 the descriptions of lifestyle and anthropometric characteristics is reported: 40% were current smokers; 42% regular drinkers; 77% of the responders were classified as normal weight and 65% changed the body weight (more than 5 kg in one year); 40% slept less than seven hours per night.

In Table 2 the means of outcome variables are reported: the values of EE, DP and PA corresponded to medium values; PCS and MCS were respectively 51.76 (SD = 7.30) and 42.55 (SD = 11.15).

Table 3 illustrates the univariate analysis. It's possible to observe associations between different outcomes (EE, DP, PA, PCS, MCS) and socio-demographic variables, lifestyle and anthropometric characteristics.

Major DP score was found in older and underweight responders, respectively p = 0.001 and p = 0.032.

In PA scale the lower score was obtained in the younger group (p = 0.004).

No significant associations were identified for EE.

The PCD score was significantly higher in male (mean (male) = 54.60 and SD = 4.63 vs mean (female) = 51.46 and SD = 6.96; p = 0.001) and in younger in comparison to older groups [mean (younger group) = and SD = 5.34 vs mean (older group) = 50.467 and SD = 7.37; p < 0.001].

Higher mean value for MCS emerged in those who didn't change their body weight more than 5 kg (46.36 with SD = 10.17 vs 41.87 with SD = 10.42; p = 0.003) and in older people (p = 0.015).

The multivariate linear regression models were reported in Table 4.

The emotional exhaustion was higher in the younger group (B = 0.148, p = 0.041) and those who sleep less than 7 hours (B = -0.171, p = 0.017).

The DP was increased in younger respondents (B = 0.296, p < 0.001), in the group that have a precarious employment of over 2 years (B = 0.190, p = 0.013), in former smokers (B = 0.162, p = 0.031) and in people who are underweight (B = 0.168, p = 0.025).

A lower value of personal accomplishment was associated with younger age (B = -0.202, p = 0.004).

The physical component score was higher in the younger group (B = 0.204, p = 0.007), but was inverse score was inversely associated with EE and DP respectively r = -0.43 (p < 0.001) and r = -0.172 (p = 0.014), and it is directly proportional to the Personal Accomplishment (r = 0.239: p = 0.001) (Figure 1).




This study found that having an atypical jobs, specifically fixed-term contracts, does not increase the quality of life and burnout.

Original articles and reviews In particular it is interesting to underline that the levels of the emotional exhaustion, depression and personal accomplishment are according to the medium values. The lifestyle of this sample was extremely different from the general population. In particular, the percentage of habitual consumption of alcohol and the smoking habit appears to be double than the general population [18, 19].

The mental and physical components appear to have significantly lower values than the general Italian population [20] (PCS = 50.03; 95% CI:49.9650.11; MCS = 41.05; 95% CI:49.99-50.15).

These results may suggest that the quality of life of the atypical workers in call centers is lower than the general population, although we cannot assert that this type of work is the cause of this worse condition. Multivariate models showed that among young people and those who have this temporary employment for more than two years, the level of depersonalization may increase significantly. You can probably assume that this type of employment status may contribute to the increase of discomfort in people.

The study has some limitations. First, the project was conducted in just two call centers and a convenience sample, and this sample is not representative of the all atypical jobs. Secondly, using the self-administered questionnaires for the collection of information, an information bias could be present due to different personal interpretations of the questions, closed answers and missing data.

Another major limitation is its cross-sectional character, which makes difficult to interpret the associations observed, especially because it cannot rule out the possibility that some people had precarious employment because of their poor health status [21, 3].

Even the choice of using the SF12 may have reduced the strength of the study. Although on the one hand have adopted this scale has made it easier for the administration of the questionnaires, and reduced the missing data, on the other hand it has certainly reduced the information that can make clearer the picture on the psychometric characteristics.

Further limitation is the heterogeneity in exposure to temporary employment refers to both qualitative and quantitative aspects. In fact, the analysis shows that this factor increases the depersonalization score.

Although there is a number of studies focusing on the relationship between atypical employment and quality of life, depression and burnout, also highlighting significant associations, the study of the relationship between precarious employment and health is still in its infancy.



According to Benach and Muntaner [22] there is need to develop a new research agenda on this topic. In fact this is an exposure in which many factors are involved at various levels and it is arduous and complex to analyze the social and health effects: gathering of quality data within improved information systems, clarification of precarious employment dimensions, development of causal theories and pathways, and creation of instruments capable of measuring the mechanisms through which precarious employment may damage workers' health.

Future researches should therefore concentrate on the investigation of multiple situations of precariousness in different social contexts and for different types of workers.



The authors are grateful to Anthony Schulman for the English revision of the manuscript.

Author's contribution statement: conceived and designed the study: AM, GLT. Collected and evaluated clinical records: AM, AN. Analyzed the data: AM, VC, AN. Wrote the paper: AM, AN, VC.



1. ISTAT. 2012. Occupati e disoccupati. II trimestre 2012 dati provvisori. Statistiche flash del 31 Agosto 2012. Available from: www.istat.it/it/archivio/69249. Last access December 2012.         

2. Virtanen M, Kivimäki M, Joensuu M, Virtanen P, Elovainio M, Vahtera J. Temporary employment and health: a review. Int J Epidemiol 2005;34(3):610-22.         

3. Santin G, Cohidon C, Goldberg M, Imbernon E. Depressive symptoms and atypical jobs in France, from the 2003 decennial health survey. Am J Ind Med 2009;52(10):799810. DOI: 10.1002/ajim.20744.         

4. Bardasi E, Francesconi M. The impact of atypical employment on individual wellbeing: evidence from a panel of British workers. Soc Sci Med 2004;58:1671-88. DOI: 10.1016/S0277-9536(03)00400-3        

5. Sirviö A, Ek E, Jokelainen J, Koiranen M, Järvikoski T, Tanila A. Precariousness and discontinuous work history in association with health. Scand J Public Health 2012;40:360-7. DOI: 10.1177/1403494812450092        

6. Ferreira Jr. M, Saldiva PHN. Computer-telephone interactive tasks: predictors of musculoskeletal disorders according to work analysis and workers' perception. Appl Ergon 2002;33:147-53. DOI: 10.1016/S00036870(01)00058-8        

7. Lin YH, Chen CY, Hong WH, Lin YC. Perceived job stress and health complaints at a bank call center: comparison between inbound and outbound services. Ind Health 2010;48:349-56. DOI: 10.2486/indhealth.48.349        

8. D'Errico A, Caputo P, Falcone U, Fubini L, Gilardi L, Mamo C, et al. Risk factors for upper extremity musculo skeletal symptoms among call center employees. J Occup Health 2010;52:115-24. DOI: 10.1539/joh.L9117        

9. Sprigg CA, Smith PR, Jackson PR. Psychosocial risk factors in call centres: an evolution of work design and well being. 2003. Research Report 169. Sudbury (UK): Health and Safety Executive (HSE) BOOKS.         

10. Costa G. The impact of shift and night work on health. Appl Ergon 27:9-16. DOI: 10.1016/0003-6870(95)00047-X        

11. Knutsson A, Akerstedt T, Jonsson BG, Orth-Gomer K. 1986. Increased risk of ischaemic heart disease in shift workers. Lancet 1996;8498(2):89-92. DOI: 10.1016/ S0140-6736(86)91619-3        

12. Morikawa Y, Nakagawa H, Miura K, Soyama Y, Ishizaki M, Kido T, et al. Shift work and the risk of diabetes mellitus among Japanese male factory workers. Scand J Work Environ Health 2005;31(3):179-83. DOI: 10.5271/ sjweh.867        

13. World Health Organization. Carcinogenicity of shiftwork, painting, and firefighting. The Lancet Oncol 2007; 8(12):1065-6.         

14. Rameshbabu A, Reddy DM, Fleming R. Correlates of negative physical health in call center shift workers. Appl Ergon 2013;44(3):350-4. DOI: 10.1016/j.apergo.2012.09.002        

15. Vandenbroucke JP, von EE, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. PLoS Med 2007; 4(10):e297. DOI: 10.1371/journal.pmed.0040297        

16. Apolone G, Mosconi P, Ware JE. Questionario sullo stato di salute SF-36. Manuale d'uso e guida all'interpretazione dei risultati. Milano: Guerini e Associati; 2000.         

17. Agostinelli A, La Torre G. Come misurare il burnout. In: De Giusti M, La Torre G (Ed.). Applicazioni di Igiene del Lavoro. Torino: Centro Scientifico Editore; 2007. P. 157-61.         

18. ISTAT. Statistiche in breve. L'uso e l'abuso di alcol in Italia. Roma: ISTAT; 2010. Available from: http://www3.istat.it/ salastampa/comunicati/non_calendario/20110405_00/. Last access January 2013.         

19. ISTAT. Abitudine al fumo. Roma: ISTAT ; 2011. Available from: http://dati.istat.it/Index.aspx?DataSetCode=DCCV_ ABTFUMO. Last access March 2013.         

20. Apolone G, Mosconi P, Quattrociocchi L, Gianicolo EAL, Groth N, Ware JE. Questionario sullo stato di salute SF-12. Versione italiana. Milano: Istituto di Ricerche Farmacologiche Mario Negri; 2005. p. 48. Available from: crc.marionegri.it/qdv/downloads/SF12%20Manuale.pdf. Last access March 2013.         

21. Virtanen P, Vahtera J, Kivimaki M, Pentti J, Ferrie J. Employment security and health. J Epidemiol Community Health 2002;56:569-74. DOI: 10.1136/jech.56.8.569        

22. Benach J, Muntaner C. Precarious employment and health: Developing a research agenda. J Epidemiol Community Health 2007;61:276-7. DOI: 10.1136/ jech.2005.045237        



Address for correspondence:
Alice Mannocci, Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza Università di Roma, Piazzale Aldo Moro 5, 00185 Rome, Italy.
E-mail: alice.mannocci@uniroma1.it.

Conflict of interest statement
The author has declared that no competing interests exist.
Received on 15 October 2013.
Accepted on 29 January 2014.

Istituto Superiore di Sanità Roma - Rome - Italy
E-mail: annali@iss.it