Cogniţie, Creier, Comportament / Cognition, Brain, Behavior Copyright © 2012Romanian Association for Cognitive Science. All rights reserved. ISSN: 1224-8398 Volume X, No. 3 (September), 423-452 SYSTEMATIC REVIEW OF BURNOUT RISK FACTORS AMONG EUROPEAN HEALTHCARE PROFESSIONALS Mara BRIA* 1 , Adriana BĂBAN 1 , Dan L. DUMITRAŞCU 2 1 Department of Psychology, Babeş-Bolyai University, Cluj-Napoca, Romania 2 University of Medicine and Pharmacy “Iuliu Haţieganu”, Cluj-Napoca, Romania ABSTRACT Healthcare professionals‟ burnout is a response to the prolonged exposure to occupational stress and affects negatively both the employee and the organization. The aim of the present review is to discuss the relevant burnout risk factors for European healthcare professionals working in hospitals and clinics. A systematic search of articles published between January 2000 and December 2011 was conducted in several databases (ISI Web of Knowledge, PsychArticles, SagePub, PubMed and Cochrane database of systematic reviews). After the analysis of the 4335 articles found, 53 met the inclusion criteria and were included in the review. Results confirm the main role of occupational and organizational risk factors while pointing out that psychosocial factors have a small yet statistically significant influence on burnout development. Socio-demographic factors, although included in the majority of studies, seem to have little impact on burnout. In conclusion, the review pointed out that although the healthcare systems across Europe are fundamentally different, healthcare professionals present similar risk factors concerning burnout. KEYWORDS: burnout, risk factors, healthcare professionals, systematic review Healthcare professionals are frequently exposed to occupational stress, especially due to overwhelming emotional and interpersonal * Corresponding author: E-mail: marabria@psychology.ro interactions. This kind of long term job strain can lead to burnout symptoms such as emotional exhaustion, depersonalization, and reduced professional efficacy (Maslach, Schaufeli, & Leiter, 2001), and may have negative consequences for both the individual and the organization. Burnout among healthcare professionals has often been associated with depression (Ahola & Hakanen, 2007), insomnia (Vela-Bueno et. al., 2008), or alcohol abuse (Moustou, Montgomery, Panagopoulou, & Benos, 2010). Professional stress affects doctors‟ and nurses‟ health. Studies indicate that professional stress is associated with inflammatory markers among physicians (Poantă, Crăciun, & Dumitraşcu, 2010) or with increased risk of cardiovascular diseases (Melamed, Shirom, Toker, Berliner, & Shapira, 2006). Burnout also jeopardizes organizational performance in medical settings. Healthcare professionals‟ burnout has been related to low performance (Keijsers, Schaufeli, Le Blanc, Zwerts, & Miranda, 1995), high turnover intentions (Leiter & Maslach, 2009), suboptimal care (Shanafelt, Bradley, Wipf, & Black, 2002), and medical errors. A recent survey shows that high levels of burnout are strongly correlated with major medical errors among American surgeons. Burnout was demonstrated to be an independent predictor of reporting medical errors, even when controlling for occupational factors like the number of overnight shifts, compensation practices, or number of working hours. More than 70% of them blamed the individual factors, and not the organization or the medical system factors (Shanafelt, et. al., 2010). The relationship between burnout and perceived medical errors is even stronger among residents. According to a longitudinal study conducted among junior doctors, all three dimensions of burnout, exhaustion, depersonalization, and reduced professional efficacy are strong predictors of perceived medical error rates reported three months later (West et al., 2006). In a meta-analysis on the link between burnout and objective performance, Taris (2006) concludes that emotional exhaustion and depersonalization have a stronger impact on reporting medical errors than on personal accomplishment. Similar results were reported by Prins et al. (2009) in a study conducted among Dutch residents from different specialties. The study also shows that perceived errors due to lack of time are more strongly linked to burnout than perceived errors due to inexperience or errors in judgment. The literature has systematically linked workload to burnout (Lee & Ashforth, 1996) and medical errors. Studies have highlighted that extended work shifts expose medical professionals to burnout (Iskera-Golec, Folkard, & Morek, 1996) and serious medical errors (Rogers, Hwang, Scott, Aiken, & Dinges, 2004). Motivated by the desire to reduce medical errors, the Accreditation Council for Graduate Medical Education limited in 2003 the working hours for American junior doctors to 80 hours a week (ACGME, 2003). Studies confirm the positive impact of these regulations. Residents were more likely to be involved in serious medical errors when they worked 24-hour shifts while the number of errors was reduced by 36% under the new regulations (Landrigan et. al., 2004). Since 1993, similar, but more restrictive, regulations were imposed by the European Commission through the European Working Time Directive (93/104/EC). The intention was to improve patient and doctorsafety by limiting progressively the maximum weekly work hours of junior doctors to 56 since 2003 and to 48 since 2009 (2003/88/EC). Studies confirm the efficiency of European Working Time Directive (EWTD) for doctors in training. Tucker and collaborators‟ study (2010) highlight that designing work schedules according to EWTD reduces doctors‟ fatigue and work – life interference. The main concerns about the implementation of EWTD were that they will be detrimental to the training of junior doctors and to the continuity of care for patients (Paice & Reid, 2004). Although there are no studies to confirm the reduced educational opportunities of junior doctors when working according to the EWTD, studies prove that working 48 hours a week does not affect patient safety (Cappuccio, Bakewell, Taggart, Ward, Ji, & Sullivan, 2009). Negative consequences of burnout on both the employee and the organization call for preventive measures in order to reduce the impact of the risk factors. Burnout prevention strategies, either addressing to the general working population (primary prevention) or the occupational groups which are more vulnerable (secondary prevention), are focused on reducing the impact of risk factors. Reviews of healthcare professionals‟ burnout focusing on identifying risk factors have been conducted previously. For example, Prins and collaborators‟ review (2007) focused on correlates of burnout among junior doctors, while other reviews focused on specific medical specialties, like palliative care (Carvalho, Pereira, & Fonseca, 2011), mental health workforce (Paris & Hoge, 2009), community mental health nursing (Edwards, Burnard, Coyle, Fothergill, & Hannigan, 2000), and cancer professionals (Trufelli et al., 2008). There are many studies about burnout risk factors among samples of European nurses (Hansen, Sverke, & Naswall, 2008; Kowalski et al., 2010) and doctors (Graham, Potts, & Ramirez, 2002; Visser, Smets, Oort, & de Haes, 2003) regardless of their medical specialties. Overview of research studies among other professional roles, such as European teachers‟ stress and burnout has been conducted previously (Rudow, 1999). But there are no studies which integrates studies about burnout risk factors among European healthcare professionals. The objective of the present review is to discuss the relevant burnout risk factors for European healthcare professionals which share the same work setting. To our knowledge, this is the first study which gathers studies of burnout risk factors among healthcare professionals working in European hospitals, regardless of their specialty or professional role. METHODS A systematic search of articles published between January 2000 and December 2011 was conducted in several databases (ISI Web of Knowledge, PsychArticles, SagePub, PubMed and Cochrane database of systematic reviews) and in the reference lists of all selected journals. The focus was to identify peer reviewed journal articles which studied the risk factors of burnout in samples of European healthcare professionals working in hospitals. The search terms used were “burnout” along with each of the following: “risk factors”, “predictors”, “causes”, “antecedents”, “medical professionals”, “doctors”, “residents”, and “nurses”, respectively. A total of 4343 abstracts resulted. After removing all the duplicates 4262 abstracts were analyzed according with the inclusion/exclusion criteria described below. When a decision could not be made based on the abstract analysis, the full text article was reviewed when available. One hundred and sixty nine full text articles were screened and in the end 53 articles matched all the inclusion criteria. The article selection steps are presented in Figure 1 and Table 1 offers a summary of the articles included in the review. The selected studies had to meet several criteria in order to be included in the analysis. Research articles which operationalized a measure of burnout or burnout dimensions were discussed. Studies had to include: 1) doctors, nurses and residents which have direct contact with patients, 2) healthcare professionals working in Europe and 3) employees of public and private hospitals or outpatient clinics. Studies which included administrative staff, healthcare personnel working in laboratories, volunteers or health care professionals without a medical training were excluded, because they can face different work stressors than professionals which have direct contact with patients. Also, studies which investigated burnout among healthcare personnel working in prisons, schools, nursing homes or home based care institutions were excluded because there are different healthcare settings which might have particular risk factors for burnout. Articles identified through database searching 4335 Articles identified from references of selected articles 8 4262 articles identified after removing duplicates 2258 articles screened 2004 articles excluded based on the exclusion criteria 169 full-text articles assessed for eligibility 116 full-text articles excluded because: 27 included personnel which does not have direct contact with patients 37 did not specify the type of medical institution 52 articles included medical personnel working in other settings than hospital and primary care clinics 53 studies included in review Figure 1. Steps of articles selection RESULTS The majority of studies included in the review adopted a crosssectional design and only three studies opted for a longitudinal design. This suggests that the majority of the European studies on burnout are focused more on describing than on explaining this phenomenon. Maslach Burnout Inventory was the most commonly used instrument, although there are many instruments designed for the evaluation of burnout (e.g., Oldenburg Burnout Inventory, the Burnout Measure, Shirom-Melamed Burnout Questionnaire, etc.). From these articles, 47 opted for Maslach Burnout Inventory – Human Services Survey (MBI-HSS) and included all three subscales in their research, while 6 applied only the emotional exhaustion scale and 3 applied both emotional exhaustion and depersonalization scales. More than half of the studies addressed healthcare professionals working in West European countries, followed by countries from the Central and Eastern Europe and by the North European countries, respectively. The majority of researches included only nurses, while about one fifth of them addressed nurses and doctors together or only doctors and residents. Socio-demographic factors The majority of the studies analyzed the role of socio-demographic variables in burnout development, e.g. country, medical specialty, hospital type, gender, age, or marital status. Although burnout rates seem to vary from country to country, about one third of the participants from the studies included in the review scored high on the burnout scales. Healthcare professionals from South - Eastern Europe shared the highest burnout rates. Almost half of the Serbian primary healthcare physicians (49% of women and 41% of men) and more than one third of Greek orthopedic nurses (38, 3 %) had high emotional exhaustion scores (Kiekkas, Spyratos, Lampa, Aretha, & Sakellaropoulos, 2010; Putnik & Houkes, 2011). Studies from the Scandinavian countries suggested that healthcare professionals are most protected from burnout, as they reported the lowest burnout rates: only 25% of Swedish nurses obtained high exhaustion and 6, 9% scored high for depersonalization (Glasberg, Eriksson, & Norberg, 2007; Gunnarsdottir, Clarke, Rafferty, & Nutbeam, 2009). These results are descriptive as few studies compared if burnout differences across countries were statistically significant. One study for example compared burnout levels between Italian and Dutch healthcare professionals and concluded that Italian healthcare professionals experienced higher burnout scores (Pisanti, van der Doef, Maes, Lazzari, & Bertini, 2011). The authors explained those differences as a consequence of unfavorable job characteristics, like high work and time pressure or high physical demands. Studies comparing burnout among specialties converged on the conclusion that healthcare professionals working in surgical areas had a higher risk of developing burnout than other medical specialties (Upton et al., 2012). Oncology personnel are more exposed to burnout in comparison to other medical specialists. Ksiazek and colleagues‟ study (2011) concluded that Polish surgical oncology nurses experienced higher burnout rates compared with general surgery nurses, while another study showed that burnout was more frequent among Italian oncology physicians and nurses than among healthcare professionals working with AIDS patients (Dorz, Novara, Sica, & Sanavio, 2003). Still, a research among UK colorectal healthcare professionals brought interesting results and pointed out that burnout was unrelated with cancer workload (Sharma, Sharp, Walker, & Monson, 2007). Looking at healthcare staff, a study pointed out that Italian dermatology nurses had a lower risk for burnout development than nurses working in general hospitals (Renzi, Tabolli, Ianni, Pietro, & Puddu, 2005). Studies focused on identifying if burnout scores varied among different hospital types offer divergent results: two Turkish studies found higher burnout rates among healthcare professionals working in public hospitals (Demir, Ulusoy, & Ulusoy, 2003; Ersoy-Kart, 2009) while a Finnish study concluded that nurses working in the university hospital experienced slightly higher burnout rates than those working in public hospital (Koivula, Paunonen, & Laippala, 2000). Two other studies analyzed burnout rates from private and public hospitals and found also divergent results. One study indicated that Swedish nurses from private hospitals experienced significantly higher burnout levels than nurses in public hospitals (Hansen, Sverke, & Naswall, 2009), while another study indicated that Turkish physicians working in private hospitals experienced the lowest burnout rates, compared to public hospitals (Ozyurt, Hayran, & Sur, 2006). Those differences may be explained by particularities of the medical systems of each country and not by hospital type. Although some studies suggested that women tend to report higher emotional exhaustion scores (Chiron, Michinov, & Olivier-Chiron, 2010), while men tend to report higher depersonalization (Klersy et al., 2007) and personal accomplishment scores (Grassi & Magnani, 2000), the majority of studies concluded that gender does not influence burnout development, neither among UK doctors (Sharma, Sharp, Walker, & Monson, 2008), nor Spanish residents (Castelo-Branco et al., 2006) or Spanish and UK nurses (Garrosa, Moreno-Jimenez, Rodrigues-Munoz, & Rodiguez-Carvajal, 2011; Losa Iglesias, de Bengoa Vallejo, & Paloma Salvadores Fuentes, 2010; Sundin, Hochwalder, Bildt, & Lisspers, 2007). The majority of studies investigating the relationship between burnout and age of healthcare professionals included age as a control variable. Results of those studies are inconclusive, as half of them found no burnout differences comparing young and senior healthcare professionals and the other half found higher depersonalization rates among young healthcare professionals (e.g., Castelo-Branco et al., 2006; Sharma et al., 2008). Marital status and burnout seems unrelated, as studies do not offer congruent results. Seven studies underlined that having a partner is a protective factor (e.g., Alacacioglu, Yavuzsen, Dirioz, Oztop, & Yilmaz, 2009) while another seven studies found no differences in burnout scores based on the marital status of participants (e.g., Panagopoulou, Montgomery, & Benos, 2006). Psychosocial factors Studies investigating the role of psychosocial factors in burnout development offered a more coherent picture than the demographical factors and highlighted that stress, personality variables, and coping mechanisms all favor burnout development. About a quarter of the studies included in the review supported the hypothesis that stress is an important predictor of burnout. While the crosssectional studies concluded that stress is associated with the development of burnout (Ahola & Hakanen, 2007; Hudek-Knezevic, Maglica, & Krapic, 2011; Sharma et al., 2007; Sharma et al., 2008), results of a longitudinal study (McManus, Winder, & Gordon, 2002) brought evidence about the causality of this relationship. Physicians‟ stress and burnout were measured at three year interval and the results pointed out that there is a reciprocal causality relationship between stress and burnout, meaning that higher stress levels cause higher burnout and higher burnout increases stress. Studies associated different coping mechanisms with burnout and highlighted that healthcare professionals who experience burnout use more emotion focused coping (e.g., substance misuse, unhealthy eating habits) or defensive coping strategies (e.g., isolating from friends and family, denying the problem or the use of humor) (Demir et al., 2003; Sharma et al., 2007; Sharma et al., 2008). For example, a study among Italian HIV/AIDS and oncology health care workers revealed that denying the problem predicted lower personal accomplishment while using humor as a coping strategy explained higher emotional exhaustion (Dorz et al., 2010). Personality variables like extraversion, optimism and neuroticism seemed to be significant but weak burnout predictors, especially for personal accomplishment dimension (Buhler & Land, 2003; HudekKnezevic et al., 2011). Hardiness as personality characteristic predicted all burnout dimensions, according to a study among a sample of Spanish nurses (Garrosa et al., 2011). Occupational factors High workload, emotional demands, work – family interference and role stress proved to be the most relevant occupational risk factors for burnout. Workload was one of the most studied occupational factors in relation to burnout defined either as quantitative demands (number of working hours, of shifts or of attended patients) or as perceived workload. The studies included in the present review indicated that the number of working hours or shifts per month contribute to burnout development. Greek residents, for example experienced higher depersonalization as working hours increased (Panagopoulou et. al., 2006), while another study indicated that emotional exhaustion in a sample of Italian dialysis healthcare professionals was affected by the number of working hours (Klersy et al., 2007). The more shifts in a month the higher the probability to experience emotional exhaustion, depersonalization, and lower personal accomplishment, according to a study among Turkish physicians (Ozyurt et al., 2006). Nurses were also affected by the weekly work duration and shifts (Ilhan, Durukan, Taner, Maral, & Bumin, 2007). Number of patient interactions per day proved to be a strong predictor for all burnout dimensions only among Spanish junior doctors (Castelo-Branco, et al., 2006). This relationship was not validated among Spanish nurses (Garrosa et al., 2011). Perceived workload might be a stronger burnout predictor than objective quantitative demands. Studies included in the review offered results to support the direct relationship between perceived high workload and burnout in nurses (Hansen et al., 2009; Kiekkas et al., 2010; Tummers, Janssen, Landeweerd, & Houkes, 2001; Tummers, Landeweerd, & van Merode, 2002), doctors (Panagopoulou et al., 2006), and both nurses and doctors (Leiter, Gascon, & Martinez-Jarreta, 2010). Panagopoulou and collaborators‟ study (2005) underlined that the evaluation of one's work is what counts most. The study highlighted that perceived workload predicted burnout after controlling the number of working hours. Emotional job demands represent emotionally overtaxing job situations like dealing with social cases, aggressive patients or facing death. Although only a few studies tested the role of emotional demands in burnout development, the results were congruent and supported its predictive role. Studies pointed out that having demanding patients increased emotional exhaustion (Escriba-Aguir & Martin-Baena, 2006) and decreased personal accomplishment (Bressi et al., 2008). A study among Swedish nurses concluded that emotional demands were a strong predictor for all burnout dimensions (Sundin et al., 2007). Emotion work is a type of emotional job demands specific to professions in which the interpersonal dimension is especially important, like health, sales or teaching. Usually, healthcare staff is encouraged to inhibit both the experience and the expression of feelings in relations to their patients but in the long run this proved to be detrimental to their wellbeing (Zapf, 2002). One study from those included in the review brought strong evidence for the role of emotion work in burnout development among a sample of Greek residents and specialists. More precisely, emotion work predicted emotional exhaustion among residents and depersonalization among specialists (Panagopoulou et al., 2006). Although work-home interference was present in only some of the studies from the current review, results pointed out that having difficulties balancing professional role with personal life fueled burnout development (Sharma et al., 2007; Sharma et al., 2008; Verdon, Merlani, Perneger, & Ricou, 2008). Some studies highlighted that work – family interference was not only a predictor for burnout but that it also mediated the relationship between job demands and burnout (Panagopolou et al., 2006). Role conflict and role ambiguity proved their predictive role for burnout. Some cross-sectional studies showed that while role ambiguity seems to account for all burnout dimensions among Turkish healthcare professionals (Tunc & Kutanis, 2009) role conflict was related only to emotional exhaustion and depersonalization (Hansen et al., 2009; Tummers et al., 2002). Nurses seemed to experience higher levels of role conflict and role ambiguity compared to physicians, at least according to a Turkish study (Tunc & Kutanis, 2009). Organizational factors Perceived job control, values incongruence, organizational justice, social support at work, effort-reward imbalance, perceived burnout complaints among colleagues and hospital organizational characteristics were all confirmed as burnout risk factors by the studies included in the present review. Perceived job control has gained attention as a burnout risk factor mainly through the Demand – Control model (Karasek, 1979; Karasek & Theorell, 1990), which promoted the concept as a key work stressor. Studies stressed that perceived control was both a proximal risk factor (EscribaAguir & Perez-Hoyos, 2007; Hansen et al., 2009; Pisanti et al., 2011; Sundin et al., 2007) or a distal burnout risk factor (Hochwalder, 2007; Tummers et al., 2002). Leiter and collaborators‟ study (2010) brought evidence to support the pivotal role of perceived job control in employees‟ work experience. More precisely, the results of their study among a sample of Spanish healthcare professionals pointed out that perceived job control is directly related to work characteristics like supervision, workload and fairness and indirectly to all three burnout dimensions. Research also highlighted that perceived values incongruence was another significant proximal risk factor for all burnout dimensions, while perceived organizational justice contributed to burnout indirectly, through perceived values. The research confirmed the mediation model of job burnout (Leiter & Maslach, 2005; Maslach & Leiter, 1997) which conceptualized burnout as a consequence of the incongruence between the employee and organization in major aspects like values, communication or fairness. The hypothesis of effort – reward imbalance model (Siegrist, 1996), according to which burnout is a consequence of the disproportion between sustained effort (extrinsic job demands and intrinsic motivation to meet those job demands) and rewards received (like salary, career opportunities, etc.) was confirmed by one study of the present review. The research pointed out that effort – reward imbalance was predictive for high emotional exhaustion and depersonalization but not for personal accomplishment among a sample of German healthcare professionals (Bakker, Killmer, Siegrist, & Schaufeli, 2000). One of the organizational factors that studies have systematically linked to the development of burnout was low social support at work, both from colleagues and supervisors. Studies pointed out that low social support from colleagues was associated especially with higher emotional exhaustion among doctors (Tummers et al., 2001), nurses (Hochwalder, 2007; Jenkins & Elliott, 2004; Sundin et al., 2007), and both doctors and nurses (EscribaAguir et al., 2006). While supervisors‟ support proved to predict burnout among healthcare professionals (Pisanti et al., 2011, Prins et al., 2007), some studies could not find any relationship between the two variables (Hansen et al., 2009). Leadership style seemed to favor burnout development, as one study suggested that transactional leadership predisposed Belgian nurses to burnout (Stordeur, D‟hoore, & Vandenberghe, 2001). Bakker and collaborators (2005) offered an interesting perspective showing that organizational social factors explain burnout development more than occupational factors. Results of their study demonstrated that burnout was more frequent among members of the same team work. As burnout was shared by the members of the same team work the authors concluded that it was somehow “contagious”. Their results pointed out that perceived burnout complaints among colleagues was the most important predictor for higher emotional exhaustion and depersonalization, even after controlling the impact of the occupational factors like job demands and decision latitude. There are also studies which highlight the role of hospital organizational characteristics such as hospital management or nurse staffing in burnout development. Emotional exhaustion among nurses was affected by doctor-nurse relationship, hospital management and organizational support, while personal accomplishment was explained only by the latter (Van Bogaert, Meulemans, Clarke, Vermeyen, & Van de Heyning, 2009). Nurse staffing also favored burnout development, as studies concluded that nurses working in Icelandic and UK hospitals with the heaviest nursepatient ratio were more likely to experience higher emotional exhaustion (Gunnarsdottir et al., 2009; Rafferty et al., 2007). DISCUSSIONS Burnout affects diverse professional categories, such as teachers (Simbula, Guglielmi & Schaufeli, 2011), police officers (Martinussen, Richardsen, & Burke, 2007), software developers (Singh, Suar, & Leiter, 2011), coaches (Hjalm, Kentta, Hassmenan, & Gustafsson, 2007) or lawyers (Tsai, Huang, & Chang, 2009). Still, burnout is the most studied among healthcare professionals. Early research suggested that healthcare professionals report higher burnout rates than other occupations. Recent studies provide information according to which there are rather different burnout patterns than occupational differences. For example, a study which compared burnout scores among five professional categories (teaching, social services, medicine, mental health and police officers) from United States and The Netherlands found no major differences in burnout levels (Schaufeli & Enzmann, 1998). Still, different burnout patterns have been identified: emotional exhaustion was higher among teachers and lower among healthcare professionals, while cynicism seems higher among police officers and lower among American mental health workers. Studies do report however burnout differences among countries, suggesting that burnout is more prevalent among North American employees than among European (Schaufeli & Buunk, 2003). Literature suggests that those differences might be attributable to cultural values; North American employees might be less reluctant to give unfavorable answers while European employees might be less likely to respond at the extremes to selfreport questionnaires (Maslach, Schaufeli, & Leiter, 2001; Schaufeli & Buunk, 2003). Although healthcare professionals‟ burnout has been extensively studied, there are only a few reviews on burnout risk factors among European professionals. Given this, the present research aims to discuss the relevant socio-demographic, psycho-social, occupational and organizational burnout risk factors among European healthcare personnel. The majority of studies investigate socio-demographic correlates of burnout, but results are not consistent and offer little support to these variables. Gender, for example, did not prove to be a risk factor for burnout, as studies included in the present review bring inconclusive results. Although there are minor gender differences in exhaustion and depersonalization scores, meta-analytic research draw a similar conclusion ruling out the role of gender in burnout development (Purvanova & Muros, 2010). Schaufeli and Buunk (2003) points out that gender differences in burnout found by some studies might be due to occupational differences. The same hypothesis may be drawn for burnout differences based on hospital type or medical specialty. Differences in burnout scores across countries highlighted by the present review are congruent with other studies. For example, a study among European family physicians pointed out that those medical professionals from South European countries obtained significantly higher burnout scores when compared to other European countries (Soler et al., 2008). Infirming the role of socio-demographic variables in burnout development offers support for models which conceptualize burnout as a consequence of occupational and organizational aspects, like The Job Demands-Resources Model (Demerouti, Nachreiner, Bakker, & Schaufeli, 2001) or The Mediation Model of Burnout (Leiter & Maslach, 2005; Maslach & Leiter, 1997). For example, differences in burnout rates across countries can be accounted by the job–demands resources model which conceptualizes burnout as a consequence of the imbalance between job pressure and available resources. Healthcare professionals working in Scandinavia (known for the lowest burnout rates across Europeans), have lower occupational pressure and more resources than those working in South – Eastern Europe. Norway has the second highest rate of nurses per 1000 population (15.47 nurses per 1000 population), while Greece has one of the lowest (with 3.27 nurses per 1000 population). Moreover, Norway has the highest rate of health expenditure per capita, with $4520, while Croatia has one of the lowest, with $358 (Schafer et al., 2010). In conclusion, as differences between burnout rates based on sociodemographic factors might be confounded with occupational or organizational differences, socio-demographic variables might best be included in future studies as control variables. Studies analyzing the role of psycho-social correlates of burnout development offer support for factors like stress, coping mechanisms and personality variables. Stress has been extensively studied in relation to burnout. Researches strongly confirm that it is a significant burnout predictor. These studies usually draw on the idea that burnout is a consequence of long-term exposure to chronic work stress. The Conservation of Resources model (Hobfoll & Shirom, 2001), The Demand Control Model (Karasek, 1979; Karasek & Theorell, 1990) or The JobDemands Resources model (Demerouti, et al., 2001) all conceptualize burnout as a strain reaction. Different but complementary approaches point out that although stress and burnout are both responses to the occupational stress, they have different antecedents and causes. Pines and Keinan (2005) propose that burnout is a consequence of questioning the importance of one‟s job. The mediation model of job burnout (Maslach & Leiter, 1997; 2005) defines burnout as an erosion of work engagement after the person experiences work dissonance between him and the organization. Although less studied, personality variables proved to be significant, but modest predictors of burnout. The results of the present review are in line with meta-analytic studies, concluding that persons high in neuroticism and low in extraversion, conscientiousness, and agreeableness are more likely to experience burnout (Alarcon, Eschleman, & Bowling, 2009; Swider & Zimmerman, 2010). Occupational factors are central antecedents and the most robust predictors of burnout in the studies included in the review. Occupational characteristics are best presented as burnout risk factors through the Job – Demands Resources model (Demerouti et al., 2001) which conceptualize burnout as a result of the imbalance between job pressures and available resources. The model was developed as a response to the simplistic (Bakker, Veldhoven, & Xanthopoulou, 2010; Jansen, Bakker, & De Jong, 2001) Demand-Control Model (Karasek, 1979; Karasek & Theorell, 1990), which defined stress as a response to a demanding job doubled by perceived low control. The Job Demands – Resources model offers a more complex and comprehensive understanding of burnout. It proposes a broader category of job demands and resources than the previous mentioned model. Workload, emotional demands and negative work-home interference are the most relevant burnout antecedents according to this model (Bakker, Demerouti, & Verbeke, 2004; Schaufeli & Bakker, 2004). De Jonge and collaborators (1999) presents results which demonstrate that the Demand-Control Model does not offer a comprehensive operationalization of job demands, especially for healthcare professional roles. The authors recommend the introduction of emotional job demands in the evaluation of health care work environment. Studies tested and confirmed the role of emotional job demands as burnout risk factors (Le Blanc, Bakker, Peeters, van Heesch, & Schaufeli, 2001; Xanthopoulou et al., 2007) and also of emotion work (de Jonge, le Blanc, Peeters, & Noordam, 2008; Zapf, Seifert, Schmutte, Mertini, & Holz, 2001). Workload proved to be the strongest predictor for emotional exhaustion (Duquette, Kerouac, Sandhu, & Beaudet, 1994; Lee & Ashforth, 1996). Literature offers support for both quantitative demands (like number of working hours or shifts) and perceived workload as burnout risk factors. Still, accumulated evidence support the subjective job experience as a strong burnout antecedent (Lee & Ashforth, 1996; Montgomery, Panagopoulos, Kehoe, & Valkanos, 2011; Schaufeli & Enzmann, 1998). Shirom and collaborators (2010) make an interesting clarification, pointing out that in burnout development perceived workload is the main determinant, while case load and work time contribute indirectly to burnout, through perceived workload. Another concept that received support as a burnout risk factor is role stress. Studies confirmed the causal effect of both role conflict and role ambiguity on burnout (Schaufeli, Bakker, van der Heijden, & Prins, 2009). Longitudinal studies highlighted that role conflict and role ambiguity explain increasing emotional exhaustion over time, while role conflict predicts depersonalization and role ambiguity predicts lower personal accomplishment (Peiro, Gonzalez-Roma, Tordera, & Manas, 2001). Perceived job control is a key concept in both the job demand control model (Karasek, 1979; Karasek & Theorell, 1990) and the mediation model of job burnout (Leiter & Maslach, 2005; Maslach & Leiter, 1997). Although the demand – control model has received support for both the role of high job demands and low perceived control in burnout development (Jonge, Janseen, & Van Breukelen, 1996), critics point out that there are few studies to confirm the interaction effect between job demands and perceived control (Bakker, Le Blanc, & Schaufeli, 2005; Demerouti, Bakker, de Jonge, Janssen, & Schaufeli, 2001; Rijk, Le Blanc, Schaufeli, & de Jonge, 1998; Taris, 2006). A complementary argument for the role of perceived job control in burnout development is brought forward by the mediation model of job burnout (Leiter & Maslach, 2005; Maslach & Leiter, 1997). More popular in US than in Europe, the model states that burnout develops as the employee perceives a mismatch between him and the organization. Burnout is, therefore, a result of the perceived incongruence between the employee and the organization in six major aspects: workload, values, community, reward, control and fairness. The model has been validated across different countries and professional roles like administrative and business services (Maslach & Leiter, 2008), health care professionals (Leiter & Maslach, 2009) or university staff (Siegal & McDonald, 2004). The model incorporates the most relevant organizational risk burnout factors: perceived job control, value congruence, supervision and social support. To summarize, studies bring consistent results to support the predictive role of perceived job control in burnout development. CONCLUSIONS The present review offers an analysis of the salient burnout risk factors for healthcare personnel working in European hospitals and clinics. In line with previous researches it confirms the main role of occupational and organizational risk factors while pointing out that psychosocial factors have a small yet statistically significant influence on burnout development. Socio-demographic factors, although included in the majority of studies, seems to have little impact on burnout. The present review has several limitations. First, as the analysis included only English-published articles, others found matching the search criteria were excluded as they had been published in other languages. Second, the majorities of studies included in the review were descriptive and focused more on describing burnout than on explaining the development processes. Third, because of the samples‟ heterogeneity it was not possible to analyze the burnout risk factors separate for nurses and doctors. Some suggestions can be made after analyzing the studies of the present review. The inconclusive results for some factors (e.g., sociodemographic) illustrate the need for more systematic designs. Longitudinal studies are needed to gather relevant data about the relation between risk factors and burnout. Factors that literature has highlighted as important burnout predictors, such as negative work – home interaction, received little attention throughout the articles included in the review. Emotion work has been widely studied in relation to burnout (Montgomery, Panagopoulou, de Wildt, & Meenks, 2006; Zapf et al., 2001) but still only one study from the present review tested this relation. Studies operationalized the job demands only through physical or emotional demands, while other job pressures were ignored. For example, there are studies which indicated cognitive demands as important burnout predictors (Peeters, Montgomery, Bakker, & Schaufeli, 2005). Organizational demands are not included either, although studies confirmed them as burnout antecedents (Bakker, Demerouti, de Boer, & Schaufeli, 2003; Xanthopoulou et al., 2007). In conclusion, the present review offers a systematic investigation of socio-demographic, psycho-social, occupational and organizational correlates of burnout and confirms the primary role of occupational factors. Although the healthcare systems across Europe are fundamentally different, the review showed that occupational factors (such as perceived job demands or job stress) and organizational characteristics (such as perceived job control or social support) are robust predictors of the burnout syndrome among different professional roles and specialties. 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Emotion work and psychological wellbeing: A review of the literature and some conceptual consideration. Human Resources Management Review, 12, 2, 237268. Zapf, D., Seifert, C., Schmutte, B., Mertini, H., & Holz, M. (2001). Emotion work and job stressors and their effects on burnout. Psychology & Health, 16, 5, 527-545. Table 1. Studies of burnout predictors included in the review: Author(s) and Country Design Sample / specialty Predictors studied Burnout dimensions EE, DE, PA Ahola and Hakanen (2007). Finland Longitudinal 3255 dentists at baseline and 3035 at three years follow-up Psychosocial factors Alacacioglu, Yavuzsen, Dirioz, Oztop, and Yilmaz (2009). Turkey Cross-sectional 133 oncology physicians and nurses Socio-demographic characteristics EE, DE, PA Alimoglu and Donmez (2005). Turkey Cross-ectional 141 nurses from university hospital Occupational and sociodemographic factors EE, DE, PA Bakker, Killmer, Siegrist, and Schaufeli (2000). Germany Cross-sectional 204 nurses from a university hospital Organizational factors EE, DE, PA Bakker, Le Blanc, and Schaufeli (2005). 12 European countries Bressi et al. (2008). Italy Cross-sectional 1849 intensive care nurses Occupational and organizational factors EE, DE, PA Cross-sectional 350 haemato oncology physicians and nurses Socio-demographic and occupational factors EE, DE, PA Buhler and Land (2003). Germany Cross-sectional 119 intensive care nurses Psychosocial variables EE, DE, PA Buunk, Ybema, van der Zee, Schaufeli, and Gibbons (2001). The Netherlands Cross-sectional 99 psychiatric nurses Organizational factors EE, DE, PA Castelo-Branco et al., (2006). Spain Cross-sectional 109 obstetrics and gynecology residents Socio-demographic and occupational factors EE, DE, PA Results There was a reciprocal relationship between burnout and depressive symptoms. Job strain predisposed to depression through burnout, while job strain predisposed to burnout directly and via depression Nurses experienced higher scores of EE. Persons younger than 29 years old experienced higher EE, DE and lower PA. Single physicians scored higher EE, DE and PA than married physicians Daylight exposure had no direct effect on burnout but it was indirectly effective via work-related stress and job satisfaction. Suffering from sleep disorders, age, having job-related health problems and educational level predicted burnout. The imbalance between extrinsic effort, low control and reward, respectively (ERI) was significantly associated with EE and DE but not with PA. Intrinsic effort moderated the relationship between ERI and EE and PA respectively, but not between ERI and DE. Perceived burnout complaints among colleagues was the strongest predictor for all burnout dimensions, after controlling high workload and low decision latitude High EE was predicted by physical tiredness. Men experienced higher DE. Low PA was explained by working with demanding patients and older age. High EE was explained by high fatalistic external locus of control, job-distance inability, existential frustration, neuroticism and extraversion. High DE was explained by high extraversion and neuroticism. Low PA was explained by high existential frustration and low extraversion. The affective consequences of social comparison were different for those with high and low personal accomplishment: for those with low personal accomplishment, a better performing colleague evoked negative feelings more often and a colleague performing worse evoked positive feelings more often. There were no gender differences in burnout. Young healthcare professionals had higher depersonalization. Single marital status and the number of patients attended in the offices per week were risk factors for burnout. Table 1. Studies of burnout predictors included in the review (continued): Author(s) and Country Design Sample / specialty Chiron, Michinov, Olivier-Chiron, Laffon, and Rusch (2010). France Demir, Ulusoy, and Ulusoy (2003) Turkey Cross sectional 151 anesthetists nurses and physicians Cross-sectional 333 hospital nurses Diez-Pinol, Dolan, Sierra, & Cannings (2008). Sweden Dorz, Novara, Sica, and Sanavio (2003). Italy Cross-sectional 1022 physicians working in public hospitals Cross-sectional 528 physicians and nurses working in oncology ad with people with AIDS Ersoy-Kart (2009). Turkey Cross-sectional Escriba-Aguir and Martin-Baena (2006). Spain Escriba-Aguir and PerezHoyos (2007). Spain Garrosa, MorenoJimenez, RodriguesMunoz, and RodiguezCarvajal (2011). Spain Predictors studied Socio-demographic, occupational and organizational factors Socio-demographic, occupational and psychosocial characteristics Burnout dimensions EE, DE, PA EE, DE, PA Socio-demographic, occupational and organizational factors Occupational and psychosocial factors Burnout 100 emergency nurses Socio-demographic, psychosocial EE, DE, PA Cross-sectional 639 emergency doctors and nurses Occupational and organizational factors EE, DE, PA Cross-sectional 639 emergency doctors and nurses Occupational and organizational factors EE Cross-sectional 508 nurses from general hospital Psychosocial factors EE, DE, PA (Nursing Burnout Scale) EE, DE, PA Results Young anesthetists and women reported higher EE than men. Status and the size of the team works were risk factors for burnout. Occupational characteristics from public hospital favored burnout development. Burnout decreased as educational level, work experience and status increased. Burnout increased when nurses were not satisfied with the working conditions, when they experienced difficulties in childcare, transportation and having economic difficulties. High levels of burnout were predicted by low job satisfaction, higher perception of salary inequity, job demands, time pressure and gender inequality. Healthcare personnel working in oncology experienced higher burnout levels than healthcare personnel working with AIDS patients. Professional status (being a doctor) and the use of humor as a coping strategy predicted EE and DE. Using planning as a coping strategy, restraint coping and denial (negative relationship) predicted PA. Nurses working in public hospitals reported lower levels of personal accomplishment. Public hospital nurses who reported that they controlled their anger reported lower DE, although trait anger levels did not cause significant differences in DE levels. Married nurses working in the public sector reported higher DE levels than unmarried nurses. High psychological demands, low job control and low supervisors‟ social support predicted burnout. Prevalence of high EE and low PA was higher among doctors. Psychosocial work environment had a different impact on physician and nurses: psychological demands increased EE among physicians and nurses; low job control and low coworkers‟ social support was associated with higher EE among physicians and low supervisors‟ social support increased EE only among nurses. There were no gender differences in burnout. Role stress (positive relationship) and hardy personality (negative relationship) predicted all burnout dimensions. Emotional competence predicted low DE and high PA. Optimism predicted low EE and high PA. Table 1. Studies of burnout predictors included in the review (continued): Author(s) and Country Design Sample / specialty Gilibert and Daloz (2008). France Glasberg, Eriksson, and Norberg (2007). Sweden Cross-sectional Grassi and Magnani (2000). Italy Predictors studied Burnout dimensions EE, DE, PA 49 nurses from a psychiatric hospital 469 healthcare personnel from a health care district Psychosocial and occupational factors Socio-demographic, occupational and psychosocial factors Cross-sectional 328 general and hospital physicians Socio-demographic characteristics EE, DE, PA Gunnarsdottir, Clarke, Rafferty, and Nutbeam (2009). Iceland Hansen, Sverke, and Naswall (2009). Sweden Cross-sectional 695 hospital nurses Organizational factors EE Cross-sectional 1102 nurses from three acute care hospitals (private for-profit, private non-profit and publicly administered) Occupational and sociodemographic factors E and CY Hochwalder (2007). Sweden Cross-sectional 1356 nurses working in hospital and primary health care centers Occupational and organizational factors EE, DE, PA Hudek-Knezevic, Maglica, and Krapic (2011). Croatia Longitudinal 118 hospital nurses Psychosocial, occupational and organizational factors EE, DE, PA Ilhan, Durukan, Taner, Maral, and Ali Bumin (2007). Turkey Cross-sectional 418 nurses from a university hospital Occupational and organizational factors EE, DE, PA Cross-sectional EE and DE Results Lower self-esteem predicted EE. DE and control predicted EE and PA. There were no age differences in burnout. Higher EE was explained by stress of conscience, gender (female), professional status (doctor), working in elder care or primary healthcare centers, low social support from co-workers and low levels of resilience. DE was explained by stress of conscience, gender (male), professional status (doctor), and lack of co-worker support. Female general practitioners reported lower scores on DE than male general practitioners. Female hospital practitioners reported lower scores on PA than male hospital practitioners. Nurse-doctor relations, unit-level support, staffing, philosophy of practice and hospital-level support predicted low EE. Women experienced higher EE than men. Burnout levels were highest at the private hospitals and lowest at the publicly administered hospital. Perceived workload and role conflict were the strongest burnout predictors from job demands. Only reduced job autonomy, goal clarity and job challenge (job resources) predicted high burnout levels. There were no gender differences in burnout rates. Young nurses experienced higher DE. Empowerment mediated the relation between social support, control and job demands on the one hand and EE, DE and PA on the other hand. There was an interaction effect between control and empowerment with regard to DE and between social support and empowerment with regard to PA. Personality traits (Big 5) predicted only reduced PA and agreeableness was the single negative predictor for PA. Organizational stress predicted EE and DE while affective normative commitment predicted low EE and high PA. There were interaction effects between personality traits and PA. The most relevant EE predictors were private life problems, perceived health, suitability of profession and relations with superiors. DE was best explained by lower professional experience, relations with superiors and colleagues and suitability of profession. PA was best explained by professional experience, relations with superiors, suitability of profession and perceived health. Table 1. Studies of burnout predictors included in the review (continued): Author(s) and Country Design Sample / specialty Predictors studied Jaworek, Marek, Karwowski, Andrzejczak, and Genaidy (2010). Poland Jenkins and Elliott (2004), UK Cross-sectional 237 nurses from four hospitals Occupational factors Burnout dimensions EE, DE Cross-sectional 93 nurses from acute mental health settings Occupational factors EE, DE, PA Kiekkas, Spyratos, Lampa, Aretha, and Sakellaropoulos (2010) Greece Cross-sectional 60 orthopedic nurses Socio-demographic and occupational factors EE, DE, PA Klersy et al., (2007). Italy Cross-sectional 344 nurses and physicians from dialysis centers Socio-demographic, occupational and organizational factors EE, DE, PA Koivula, Paunonen, and Laippala (2000). Finland Cross-sectional 723 nurses from two hospitals Socio-demographic and occupational characteristics Ksiazek, Stefaniak, Stadnyk, and Ksiazek (2011). Poland Leiter, Gascon, and Martinez-Jarreta (2010). Spain Losa Iglesias, de Bengoa Vallejo, and Fuentes (2010). Spain Cross-sectional 60 surgery nurses Occupational and sociodemographic factors Enthusiasm about nursing, incipient burnout, frustration and burnout EE, DE, PA Cross-sectional 1477 doctors and nurses from 3 hospitals Organizational factors E, CY, IN Cross-sectional 80 critical care nurses Socio-demographic factors EE, DE, PA McManus, Winder, and Gordon (2002). UK Longitudinal Psychosocial factors EE, DE, PA Ozyurt, Hayran, and Sur (2006). Turkey Cross-sectional 551 hospital based and family practitioner physicians at T1 and 331 after 3 years 598 physicians Socio-demographic and occupational factors EE, DE, PA Results EE was explained by stress. Higher DE was explained by higher stressors for nurses who reported high levels of support. There were no age, marital status or gender differences in burnout. Low work satisfaction, having difficulty in meeting patient care needs, perceived unsatisfactory relations with physicians and in their private life explained all burnout dimensions. Perceived high workload explained high EE. There were no differences in burnout scores between physicians and nurses. EE was predicted by workload. Men experience higher DE scores. DE is explained by bad relationships with co-workers. Low PA was explained by having no children and having a permanent hospital position. Burnout increased with age and years of professional experience. Burnout was higher among healthcare professionals from university hospital and from psychiatry ward. Burnout was explained by work demands (positive relationship) and work stimuli (negative relationship). No significant differences concerning burnout dimensions between nurses from the two specialties but overall intensity of burnout was significantly higher among oncology nurses. Work environment (values, manageable workload, control, supervision and fairness) predicted burnout. EE was higher among nurses older than 31 years old and with work experience. DE was explained by higher work experience. PA was higher among married nurses. There was a reciprocal causal relationship between stress and EE DE reduced stress, while PA increased stress, both directly and indirectly by increasing EE. Male experienced higher DE than women. Age, professional status, being single, number of vacations per year and number of shifts per month were burnout risk factors. Table 1. Studies of burnout predictors included in the review (continued): Author(s) and Country Design Sample / specialty Predictors studied Burnout dimensions EE and DE Panagopoulou, Montgomery, and Benos. (2006). Greece Cross-sectional 244 residents and specialists from two public hospitals Occupational and psychosocial factors Pisanti, van der Doef, Maes, Lazzari, and Bertini (2011) Italy and The Netherlands Cross-sectional 609 Italian nurses and 873 Dutch nurses Socio-demographic, occupational and organizational factors EE, DE, PA Popa et al., (2010).Romania Cross-sectional 263 emergency doctors Socio-demographic factors EE, DE, PA Prins et al., (2007). The Netherlands Cross-sectional 158 residents Occupational factors E, CY Putnik and Houkes (2011). Serbia Quattrin et al., (2006). Italy Cross-sectional Socio-demographic factors EE, DE, PA Cross-sectional 373 primary health care physicians 100 oncology nurses Socio-demographic and organizational factors EE, DE, PA Rafferty et al., (2007). UK Cross-sectional 3984 nurses Occupational factors EE Renzi, Tabolli, Ianni, Pietro, and Puddu (2005). Italy Cross-sectional 344 dermatology and general physicians and nurses Occupational factors EE, DE, PA Sharma, Sharp, Walker, and Monson (2007) UK Cross-sectional 430 colorectal surgeons and nurses Socio-demographic, psychosocial and occupational variables EE, DE, PA Sharma, Sharp, Walker, and Monson (2008). UK Cross-sectional 501 colorectal and vascular surgeons Psychosocial and occupational factors EE, DE, PA Results There were no marital status or gender differences in burnout Physicians‟ EE was predicted only by perceived job demands, Residents‟ EE was predicted only by emotional labor. Physicians‟ DE was predicted only by emotional labor, Residents‟ DE was predicted by number of worked hours/week. Italian nurses experienced higher EE, DE and PA levels than Dutch nurses. Work/time pressure, skill discretion and supervisors „support were the strongest predictors for EE. Skill discretion and material resources were DE predictors. Men and younger healthcare professional had higher DE. Decision authority predicted only personal accomplishment. Burnout was not explained by age, marital status or gender. EE increased with years of professional experience. EE was explained by dissatisfaction with emotional and appreciative support from supervisors. CY was explained by dissatisfaction with emotional support from supervisors Women experienced more DE than men. Senior nurses scored higher EE levels. Organizational strategy explained EE while personal strategy explained DE and PA. Nurse from hospitals with the highest patient loads (patient-tonurse ratio) were 71% more likely to develop burnout than hospitals with the most favorable nurse staffing. Job satisfaction was negatively associated with burnout, regardless of clinical specialty. Dermatology nurses had a lower burnout risk than other specialties; their risk for burnout increased with longer duration of employment in the same hospital. Among physicians age was negatively associated with burnout. There were no marital status, age or gender differences in burnout Surgeons had higher levels of DE than nurses. Defensive coping strategies, reduced satisfaction with work, training in management or communication and intention to retire before statutory age predicted burnout. Defensive coping strategies, reduced satisfaction with work, training in management or communication and intention to retire before statutory age predicted burnout. Table 1. Studies of burnout predictors included in the review (continued): Author(s) and Country Design Sample / specialty Predictors studied Burnout dimensions EE Stordeur, D‟hoore, and Vandenberghe (2001). Belgium Sundin, Hochwalder, Bildt, and Lisspers (2007). Sweden Cross-sectional 625 nurses from a university hospital Occupational and organizational factors Cross-sectional 1561 nurses from three hospitals and two primary health care centers Socio-demographic, occupational and psychosocial factors EE, DE, PA Tselebis, Moulou, and Ilias (2001). Greece Cross-sectional Socio-demographic factors EE, DE, PA Tummers, Janssen, Landeweerd, and Houkes (2001). The Netherlands Tummers, Landeweerd, and van Merode (2002). The Netherlands Tunc and Kutanis (2009). Turkey Cross-sectional 79 nurses in internal, respiratory medicine and general surgery 374 general and psychiatric hospital nurses Occupational factors EE Cross-sectional 1204 nurses working in general hospitals Organizational and occupational factors EE Cross-sectional 251 physicians and nurses from a university hospital Socio-demographic and occupational factors EE, DE, PA Van Bogaert, Meulemans, Clarke, Vermeyen, and van der Heyning (2009). Belgium Verdon, Merlani, Perneger, and Ricou (2008). Switzerland Cross-sectional 401 hospital nurses from medical, surgical and intensive care units Socio-demographic and organizational factors EE, DE, PA Cross-sectional 97 surgical nurses Socio-demographic and occupational factors EE, DE, PA EE: emotional exhaustion; DE: depersonalization; PA: personal accomplishment; E: exhaustion; CY: cynicism; IN: inefficacy Results Workload, conflict with supervisors / colleagues, role ambiguity, role conflict and transactional leadership were predictors for EE. There were no marital status or gender differences in burnout. Age (negative relationship) predicted EE and DE. Number of children (negative relationship), job demands (positive relationship) and job control (negative relationship) predicted EE and DE. Co-worker and patient support predicted all burnout dimensions, while supervisor‟s support predicted only EE. Men have higher personal achievements scores than women. There were no marital or gender differences on EE and DE. dimensions. Perceived workload and low social support predicted EE. Mental health nurses experienced higher levels of EE than general nurses. Complexity and decision authority had an indirect effect on EE, through workload (for complexity) and through role ambiguity and social support (for decision authority). Nurses experienced higher burnout levels than physicians Role ambiguity explained all burnout dimensions while role conflict explained EE and DE. Nurse – physician relationship, hospital management and organizational support had a direct impact on EE. Hospital management and organizational support had a positive direct effect on PA. Lack of patients‟ cooperation, the organization of the work and the rapid patient turnover were significant independent burnout factors. Demographic characteristics did not predict burnout.