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

1Department of Psychology, Babeş-Bolyai University, Cluj-Napoca, Romania

2University 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 doctor-

safety 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.

Figure 1. Steps of articles selection

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

RESULTS

The majority of studies included in the review adopted a cross-

sectional 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 cross-

sectional 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; Hudek-

Knezevic 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 well-

being (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 (Escriba-

Aguir & 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 (Escriba-

Aguir 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 nurse-

patient 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 self-

report 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 socio-

demographic 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 Job-

Demands 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., socio-

demographic) 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.

ACKNOWLEDGEMENTS

This paper was supported by THE SECTORAL OPERATIONAL PROGRAM FOR

HUMAN RESOURCES DEVELOPMENT via the POSDRU contract 88/1.5/S/56949 –

“Reform project of the doctoral studies in medical sciences: an integrative vision from

financing and organization to scientific performance and impact.

This paper was partly supported by the European Union Framework Seven (EU-FP7

Health) via the project “Improving quality and safety in the hospital: The link between

organisational culture, burnout and quality of care”.

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Table 1. Studies of burnout predictors included in the review:

Author(s) and Country Design Sample / specialty Predictors studied Burnout

dimensions

Results

Ahola and Hakanen

(2007). Finland

Longitudinal 3255 dentists at baseline

and 3035 at three years

follow-up

Psychosocial factors EE, DE, PA 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

Alacacioglu, Yavuzsen,

Dirioz, Oztop, and

Yilmaz (2009). Turkey

Cross-sectional 133 oncology physicians

and nurses

Socio-demographic

characteristics

EE, DE, PA 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

Alimoglu and Donmez

(2005). Turkey

Cross-ectional 141 nurses from university

hospital

Occupational and socio-

demographic factors

EE, DE, PA 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.

Bakker, Killmer,

Siegrist, and Schaufeli

(2000). Germany

Cross-sectional 204 nurses from a

university hospital

Organizational factors EE, DE, PA 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.

Bakker, Le Blanc, and

Schaufeli (2005).

12 European countries

Cross-sectional 1849 intensive care nurses Occupational and

organizational factors

EE, DE, PA Perceived burnout complaints among colleagues was the

strongest predictor for all burnout dimensions, after controlling

high workload and low decision latitude

Bressi et al. (2008).

Italy

Cross-sectional 350 haemato oncology

physicians and nurses

Socio-demographic and

occupational factors

EE, DE, PA High EE was predicted by physical tiredness.

Men experienced higher DE.

Low PA was explained by working with demanding patients

and older age.

Buhler and Land (2003).

Germany

Cross-sectional 119 intensive care nurses Psychosocial variables EE, DE, PA 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.

Buunk, Ybema, van der

Zee, Schaufeli, and

Gibbons (2001).

The Netherlands

Cross-sectional 99 psychiatric nurses Organizational factors EE, DE, PA 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.

Castelo-Branco et al.,

(2006). Spain

Cross-sectional 109 obstetrics and

gynecology residents

Socio-demographic and

occupational factors

EE, DE, PA 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 Predictors studied Burnout

dimensions

Results

Chiron, Michinov,

Olivier-Chiron, Laffon,

and Rusch (2010). France

Cross sectional 151 anesthetists nurses and

physicians

Socio-demographic,

occupational and

organizational factors

EE, DE, PA Young anesthetists and women reported higher EE than men.

Status and the size of the team works were risk factors for

burnout.

Demir, Ulusoy, and

Ulusoy (2003) Turkey

Cross-sectional 333 hospital nurses Socio-demographic,

occupational and

psychosocial characteristics

EE, DE, PA 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.

Diez-Pinol, Dolan,

Sierra, & Cannings

(2008). Sweden

Cross-sectional 1022 physicians working in

public hospitals

Socio-demographic,

occupational and

organizational factors

Burnout High levels of burnout were predicted by low job satisfaction,

higher perception of salary inequity, job demands, time

pressure and gender inequality.

Dorz, Novara, Sica, and

Sanavio (2003). Italy

Cross-sectional 528 physicians and nurses

working in oncology ad

with people with AIDS

Occupational and

psychosocial factors

EE, DE, PA 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.

Ersoy-Kart (2009).

Turkey

Cross-sectional 100 emergency nurses Socio-demographic,

psychosocial

EE, DE, 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.

Escriba-Aguir and

Martin-Baena (2006).

Spain

Cross-sectional 639 emergency doctors and

nurses

Occupational and

organizational factors

EE, DE, PA High psychological demands, low job control and low

supervisors‟ social support predicted burnout.

Prevalence of high EE and low PA was higher among doctors.

Escriba-Aguir and Perez-

Hoyos (2007). Spain

Cross-sectional 639 emergency doctors and

nurses

Occupational and

organizational factors

EE Psychosocial work environment had a different impact on

physician and nurses: psychological demands increased EE

among physicians and nurses; low job control and low co-

workers‟ social support was associated with higher EE among

physicians and low supervisors‟ social support increased EE

only among nurses.

Garrosa, Moreno-

Jimenez, Rodrigues-

Munoz, and Rodiguez-

Carvajal (2011). Spain

Cross-sectional 508 nurses from general

hospital

Psychosocial factors EE, DE, PA

(Nursing

Burnout

Scale)

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 Predictors studied Burnout

dimensions

Results

Gilibert and Daloz (2008).

France

Cross-sectional 49 nurses from a psychiatric

hospital

Psychosocial and

occupational factors

EE, DE, PA Lower self-esteem predicted EE.

DE and control predicted EE and PA.

Glasberg, Eriksson, and

Norberg (2007). Sweden

Cross-sectional 469 healthcare personnel

from a health care district

Socio-demographic,

occupational and psycho-

social factors

EE and DE 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.

Grassi and Magnani

(2000). Italy

Cross-sectional 328 general and hospital

physicians

Socio-demographic

characteristics

EE, DE, PA Female general practitioners reported lower scores on DE than

male general practitioners.

Female hospital practitioners reported lower scores on PA than

male hospital practitioners.

Gunnarsdottir, Clarke,

Rafferty, and Nutbeam

(2009). Iceland

Cross-sectional 695 hospital nurses Organizational factors EE Nurse-doctor relations, unit-level support, staffing, philosophy

of practice and hospital-level support predicted low EE.

Hansen, Sverke, and

Naswall (2009). Sweden

Cross-sectional 1102 nurses from three

acute care hospitals (private

for-profit, private non-profit

and publicly administered)

Occupational and socio-

demographic factors

E and CY 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.

Hochwalder (2007).

Sweden

Cross-sectional 1356 nurses working in

hospital and primary health

care centers

Occupational and

organizational factors

EE, DE, PA 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.

Hudek-Knezevic,

Maglica, and Krapic

(2011). Croatia

Longitudinal 118 hospital nurses Psychosocial, occupational

and organizational factors

EE, DE, 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.

Ilhan, Durukan, Taner,

Maral, and Ali Bumin

(2007). Turkey

Cross-sectional 418 nurses from a

university hospital

Occupational and

organizational factors

EE, DE, 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 Burnout

dimensions

Results

Jaworek, Marek,

Karwowski, Andrzejczak,

and Genaidy (2010). Poland

Cross-sectional 237 nurses from four

hospitals

Occupational factors EE, DE Burnout was explained by work demands (positive

relationship) and work stimuli (negative relationship).

Jenkins and Elliott (2004),

UK

Cross-sectional 93 nurses from acute mental

health settings

Occupational factors EE, DE, PA EE was explained by stress.

Higher DE was explained by higher stressors for nurses who

reported high levels of support.

Kiekkas, Spyratos, Lampa,

Aretha, and Sakellaropoulos

(2010) Greece

Cross-sectional 60 orthopedic nurses Socio-demographic and

occupational factors

EE, DE, PA 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.

Klersy et al., (2007). Italy Cross-sectional 344 nurses and physicians

from dialysis centers

Socio-demographic,

occupational and

organizational factors

EE, DE, PA 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.

Koivula, Paunonen, and

Laippala (2000). Finland

Cross-sectional 723 nurses from two

hospitals

Socio-demographic and

occupational characteristics

Enthusiasm

about nursing,

incipient

burnout,

frustration and

burnout

Burnout increased with age and years of professional

experience.

Burnout was higher among healthcare professionals from

university hospital and from psychiatry ward.

Ksiazek, Stefaniak,

Stadnyk, and Ksiazek

(2011). Poland

Cross-sectional 60 surgery nurses Occupational and socio-

demographic factors

EE, DE, PA No significant differences concerning burnout dimensions

between nurses from the two specialties but overall intensity of

burnout was significantly higher among oncology nurses.

Leiter, Gascon, and

Martinez-Jarreta (2010).

Spain

Cross-sectional 1477 doctors and nurses

from 3 hospitals

Organizational factors E, CY, IN Work environment (values, manageable workload, control,

supervision and fairness) predicted burnout.

Losa Iglesias, de Bengoa

Vallejo, and Fuentes

(2010). Spain

Cross-sectional 80 critical care nurses Socio-demographic factors EE, DE, PA 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.

McManus, Winder, and

Gordon (2002). UK

Longitudinal 551 hospital based and

family practitioner

physicians at T1 and 331

after 3 years

Psychosocial factors EE, DE, PA There was a reciprocal causal relationship between stress and

EE

DE reduced stress, while PA increased stress, both directly and

indirectly by increasing EE.

Ozyurt, Hayran, and Sur

(2006). Turkey

Cross-sectional 598 physicians Socio-demographic and

occupational factors

EE, DE, PA 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

Results

Panagopoulou,

Montgomery, and Benos.

(2006). Greece

Cross-sectional 244 residents and specialists

from two public hospitals

Occupational and

psychosocial factors

EE and DE 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.

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 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.

Popa et al., (2010).Romania Cross-sectional 263 emergency doctors Socio-demographic factors EE, DE, PA Burnout was not explained by age, marital status or gender.

EE increased with years of professional experience.

Prins et al., (2007). The

Netherlands

Cross-sectional 158 residents Occupational factors E, CY EE was explained by dissatisfaction with emotional and

appreciative support from supervisors.

CY was explained by dissatisfaction with emotional support

from supervisors

Putnik and Houkes (2011).

Serbia

Cross-sectional 373 primary health care

physicians

Socio-demographic factors EE, DE, PA Women experienced more DE than men.

Quattrin et al., (2006). Italy Cross-sectional 100 oncology nurses Socio-demographic and

organizational factors

EE, DE, PA Senior nurses scored higher EE levels.

Organizational strategy explained EE while personal strategy

explained DE and PA.

Rafferty et al., (2007). UK Cross-sectional 3984 nurses Occupational factors EE Nurse from hospitals with the highest patient loads (patient-to-

nurse ratio) were 71% more likely to develop burnout than

hospitals with the most favorable nurse staffing.

Renzi, Tabolli, Ianni,

Pietro, and Puddu (2005).

Italy

Cross-sectional 344 dermatology and

general physicians and

nurses

Occupational factors EE, DE, PA 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.

Sharma, Sharp, Walker, and

Monson (2007) UK

Cross-sectional 430 colorectal surgeons and

nurses

Socio-demographic,

psychosocial and

occupational variables

EE, DE, PA 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.

Sharma, Sharp, Walker, and

Monson (2008). UK

Cross-sectional 501 colorectal and vascular

surgeons

Psychosocial and

occupational factors

EE, DE, PA 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

Results

Stordeur, D‟hoore, and

Vandenberghe (2001).

Belgium

Cross-sectional 625 nurses from a

university hospital

Occupational and

organizational factors

EE Workload, conflict with supervisors / colleagues, role

ambiguity, role conflict and transactional leadership were

predictors for EE.

Sundin, Hochwalder, Bildt,

and Lisspers (2007).

Sweden

Cross-sectional 1561 nurses from three

hospitals and two primary

health care centers

Socio-demographic,

occupational and psycho-

social factors

EE, DE, PA 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.

Tselebis, Moulou, and Ilias

(2001). Greece

Cross-sectional 79 nurses in internal,

respiratory medicine and

general surgery

Socio-demographic factors EE, DE, PA Men have higher personal achievements scores than women.

There were no marital or gender differences on EE and DE.

dimensions.

Tummers, Janssen,

Landeweerd, and Houkes

(2001). The Netherlands

Cross-sectional 374 general and psychiatric

hospital nurses

Occupational factors EE Perceived workload and low social support predicted EE.

Mental health nurses experienced higher levels of EE than

general nurses.

Tummers, Landeweerd, and

van Merode (2002). The

Netherlands

Cross-sectional 1204 nurses working in

general hospitals

Organizational and

occupational factors

EE Complexity and decision authority had an indirect effect on EE,

through workload (for complexity) and through role ambiguity

and social support (for decision authority).

Tunc and Kutanis (2009).

Turkey

Cross-sectional 251 physicians and nurses

from a university hospital

Socio-demographic and

occupational factors

EE, DE, PA Nurses experienced higher burnout levels than physicians

Role ambiguity explained all burnout dimensions while role

conflict explained EE and DE.

Van Bogaert, Meulemans,

Clarke, Vermeyen, and van

der Heyning (2009).

Belgium

Cross-sectional 401 hospital nurses from

medical, surgical and

intensive care units

Socio-demographic and

organizational factors

EE, DE, PA 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.

Verdon, Merlani, Perneger,

and Ricou (2008).

Switzerland

Cross-sectional 97 surgical nurses Socio-demographic and

occupational factors

EE, DE, 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.

EE: emotional exhaustion; DE: depersonalization; PA: personal accomplishment; E: exhaustion; CY: cynicism; IN: inefficacy