Predictors of emotional exhaustion, disengagement and burnout among

Improving Access to Psychological Therapies (IAPT) practitioners

Short title: Burnout in IAPT workers

Abstract=196 words; Main text=3981; Tables=4

Sophie Westwood1, Linda Morison1, Jackie Allt2, Nan Holmes1

1 School of Psychology, University of Surrey, Guildford, UK

2 Time to Talk, Sussex Community NHS Trust, Horsham, UK

Correspondence: Linda Morison, School of Psychology, University of Surrey, Guildford, UK. E-mail: l.morison@surrey.ac.uk . Tel: 01483 686875

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Predictors of emotional exhaustion, disengagement and burnout among

Improving Access to Psychological Therapies (IAPT) practitioners

Abstract

Background: Among mental health staff, burnout has been associated with undesirable outcomes

such as physical and mental ill-health, high levels of staff turnover and poorer patient care.

Aims: To estimate the prevalence and predictors of burnout amongst Improving Access to

Psychological Therapist (IAPT) practitioners.

Method: IAPT practitioners (N=201) completed an on-line survey measuring time spent per week on

different types of work related activity. These were investigated as predictors of burnout (measured

using the Oldenburg Burnout Inventory).

Results: The prevalence of burnout was 68.6% (95% confidence interval (CI) 58.8% - 77.3%) among

Psychological Wellbeing Practitioners (PWP) and 50.0% (95%CI 39.6% - 60.4%) among High Intensity

(HI) therapists. Among PWPs hours of overtime predicted higher odds of burnout and hours of

clinical supervision predicted lower odds of burnout. The odds of burnout increased with telephone

hours of patient contact among PWPs who had worked in the service for two or more years. None of

the job characteristics significantly predicted burnout among HI therapists.

Conclusions: Our results suggest a high prevalence of burnout among IAPT practitioners. Strategies

to reduce burnout among PWPs involving reductions in workload, particularly telephone contact,

and increases in clinical supervision need to be evaluated.

Acknowledgements: The authors wish to thank Andrew Barnes for his help in setting up the on-line

survey.

Conflict of Interest: The authors declare that they have no conflict of interest with respect to this

publication.

Keywords: Burnout, emotional exhaustion, disengagement, IAPT, mental health

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Introduction

Burnout was first described in the 1970s as a psychological syndrome occurring after exposure to

long term emotional and interpersonal stress at work (Freudenberger, 1974) and has been

conceptualised in numerous ways since. Maslach and others reviewed the concept of burnout and

its measurement and developed the most commonly used conceptualization and measure (Maslach,

Schaufeli, & Leiter, 2001). They proposed that burnout is composed of three components: feelings of

emotional exhaustion, depersonalisation and reduced personal accomplishment and provide a

measure for each in the Maslach Burnout Inventory (MBI) (Maslach, Jackson, & Leiter, 1996).

However, the personal accomplishment component of burnout has been questioned on the grounds

that there is less evidence for it when the factor structure is examined and that it might better be

considered a consequence of burnout (Schaufeli, Bakker, Hoogduin, Schaap, & Kladler, 2001).

Demerouti and others therefore developed a measure based on a two factor structure with the

components, emotional exhaustion and disengagement, devised the Oldenburg Burnout Inventory

(OLBI) as a measure (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). Additional advantages of

the OLBI are that it uses both positively and negatively worded items within each component

(whereas the MBI has all items phrased in the same direction within each component) and captures

a broader conceptualisation of burnout by incorporating both cognitive and physical elements of

exhaustion (Halbesleben & Demerouti, 2005). The MBI and OLBI both result in scores for each of the

components of burnout rather than indicating whether a person is suffering from burnout at a

clinically problematic level or not. Various approaches have been used to develop cutoff values

which identify problematic burnout from the inventory scores (Kleijweg, Verbraak, & Van Dijk, 2013;

Peterson, Demerouti, Bergstrom, Asberg, & Nygren, 2008) but these have not undergone extensive

validation and there are indications that they may vary by country (Schaufeli & Vandierendonck,

1995). This greatly hinders the estimation of the prevalence of clinical burnout within work-forces.

However, a recent review of studies using existing cut-off values to define burnout (Morse, Salyers,

Rollins, Monroe-DeVita, & Pfahler, 2012) found prevalences between 21% and 67% among mental

health workers. These high prevalences are concerning because research findings have consistently

indicated an association between burnout and undesirable outcomes such as physical and mental ill-

health and high levels of staff turnover (Morse et al., 2012; Paris & Hoge, 2010). There is also

evidence that burnout can negatively affect the care received by patients (Morse et al., 2012) so

could be contributing to negligence and lack of compassionate care within parts of the United

Kingdom’s National Health Service (NHS) (Francis, 2013).

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The ‘Improving Access to Psychological Therapies’ (IAPT) programme is a relatively new, large scale

initiative that aims to greatly increase the availability of National Institute for Health and Care

Excellence (NICE) recommended psychological treatment within the NHS. Originally launched in

2008 to treat anxiety disorders and depression in adults, the range of ages and conditions treated

within the IAPT programme has expanded and continues to grow. Cognitive-Behavioural Therapy

(CBT) is the predominant therapeutic approach used within the programme and treatment is

delivered at two levels. ‘Low intensity’ high volume interventions are delivered by Psychological

Wellbeing Practitioners (PWPs) following a detailed protocol. ‘High-intensity’ (HI) interventions are

mainly delivered by trainee or accredited CBT therapists. IAPT practitioners are obviously exposed to

the potential for burnout experienced by mental health practitioners generally, but aspects of IAPT

work such as the composition of workloads and the focus on meeting targets for client recovery may

be specific risks for them. One aim of this study was therefore to estimate the prevalence of

burnout in this group of mental health practitioners.

Most research on the correlates and antecedents of burnout suggests that organizational-

environmental factors are more strongly predictive of burnout than individual characteristics such as

age, sex or ethnicity (Morse et al., 2012). Organizational factors found to be associated with burnout

(or its components emotional exhaustion and disengagement/depersonalisation) include excessive

workload, being under time pressure, role ambiguity, lack of supervisory and colleague support,

limited decision-making in factors affecting the employee, lack of autonomy, unfairness or inequity

in the workplace and insufficient rewards (including social recognition) (Morse et al., 2012). A second

aim of this study was therefore to examine which individual and job characteristics predicted

emotional exhaustion, disengagement and burnout among IAPT practitioners. Burnout is

conceptualized as being the result of stress experienced over time so years of work in the IAPT

service was also considered as a risk factor and potential moderator of the effect of other predictors.

Methods

Study design

This was a cross-sectional survey design.

Study Population

Participants were IAPT practitioners working ≥35 hours a week recruited via IAPT services in the

South of England or through the website or magazine of the main professional body for IAPT

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practitioners – The British Association of Behavioural and Cognitive Psychotherapies. It was

estimated that a sample size of 100 would allow a prevalence of burnout of 60% to be estimated

with a precision (95% confidence interval) of ± 10%; and for a difference between two equal sized

groups in proportion with burnout of 23% to be detected at the 5% significance level with 80%

power.

Measures

Burnout. The Oldenburg Burnout Inventory (OLBI) measuring emotional exhaustion and

disengagement, developed to overcome the limitations of the MBI mentioned above, was used for

this study. Each of the two components is measured using eight items such as “there are days when I

feel tired before I arrive at work” for emotional exhaustion and “lately I tend to think less at work

and do my job almost mechanically” for disengagement. Participants respond by choosing one of

four responses from “strongly agree” to “strongly disagree”. For calculating mean scores for each of

the two components, items were reversed where necessary so that a higher score indicated more

exhaustion or disengagement. In the present study, the Cronbach’s alpha coefficient was .86 for

exhaustion and .83 for disengagement.

The binary variable indicating whether or not the participant was suffering from problematic

burnout was created using cut-off scores on the OLBI which correspond to those on the MBI found

to best predict physician-diagnosed burnout (Peterson et al., 2008). Using these cut-off scores,

burnout was indicated by a score ≥2.25 for exhaustion and a score ≥2.10 for disengagement.

Demographic data. The demographic data collected consisted of age, gender and ethnicity.

Information was gathered on the number of years of mental health experience prior to and then in

the current service.

Job characteristics. The data collected consisted of: job role (PWP or HI therapist); hours spent

providing clinical supervision or case management; caseload (number of patients); hours of patient

contact per week including by telephone, face to face and group contact; hours spent inputting data

per week; hours of overtime per week; whether participants had their own desk; awareness of IAPT

targets (using a likert scale from 1 to 5); and hours of clinical supervision or case management

received each week. Because previous studies identified organisational issues and conflicts with

colleagues as predictors of burnout, data were collected using the Mental Health Professionals

Stress Scale (Cushway, Tyler, & Nolan, 1996). The items were presented as different sources of

pressure at work and respondents were asked to rate the extent to which each item applied to them

on a four-point likert scale. Cronbach’s alpha for the scale for this study was .94. Two subscales (with

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6 items each) of ‘Organisational structure and processes’ and ‘Relationships and conflicts with other

professionals’ were included in the analysis presented here. An example item from the

Organisational structures subscale is “lack of support from management” while an example from the

Relationships subscale is “criticism by other professionals eg doctor, nurse, colleague”. Items were

reversed where necessary and then average scores for each subscale were calculated, with higher

scores indicating more difficulties.

Data Collection Procedure

Participants were recruited through two routes during 2011. The first method involved emailing a

link to the on-line survey to eligible staff within 15 IAPT services in the South of England for which

both NHS Research and Development and service permission was obtained. Two reminder emails

were sent and within each service five paper copies of the questionnaire with stamped addressed

envelopes were also provided for people who preferred this method of data collection. The second

route was via an advertisement which was displayed in the magazine “CBT Today” published by the

British Association for Behavioural and Cognitive Psychotherapies. The advertisement was also

displayed on their website. For all routes of data collection the participant information sheet

presented the survey as “researching different aspects of an IAPT worker’s role and their

experiences of their work” in order to avoid bias due to those feeling more negative about their

work being more likely to participate.

Ethical Approval

Applications to conduct the study were submitted to 8 NHS Research and Development departments

through the Integrated Research Application System and were given approval. A further 3

applications were submitted through local application systems of which one was rejected due to

differing research priorities. This resulted in the participation of 15 IAPT services as there was more

than one service in some of the trusts. Ethical approval was also granted by the Faculty of Arts and

Human Sciences Ethics Committee at the University of Surrey (735-PSY-12).

Data Analysis

Range checks were incorporated into the on-line questionnaire and any paper questionnaires were

checked manually before computer entry. SPSS (version 20, IBM) was used for descriptive analysis

and Stata (version 14, Statacorps) was used for regression modelling. Exact 95% confidence intervals

were calculated for prevalences of burnout using the on-line calculator

http://statpages.org/confint.html .

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Regression modelling was used to identify predictors of emotional exhaustion and disengagement.

First, simple linear regression models were fitted for each predictor for each of the two outcome

variables. Then any independent variables which predicted the outcome with p<0.2 were included in

a multiple regression model. The cut-off value of 0.2 was chosen because it ensures that important

variables which might need to be controlled for are included in the model and these variables might

be missed if it was set at a lower level (Kirkwood & Sterne, 2003). Predictors which had the largest

p-values for the regression slope were then excluded manually until a set of independent predictors

was obtained. In order to examine whether length of time in the present service moderated the

effect of any of the job characteristic predictors, the above procedure was repeated including

interaction terms with a binary variable which indicated whether the person had worked in the

service for less than two years vs. two or more years. A similar analysis strategy was followed with

burnout as the dependent variable but because this was a binary variable, logistic regression

modelling was used.

Results

Response rate

Consultation with managers indicated that there were around 634 IAPT practitioners in the 15 IAPT

services that participated and 212 practitioners (33.4%) were recruited through this route. A further

50 were recruited through the British Association for Behavioural and Cognitive Psychotherapists

website or magazine making a total number of 262. Participants who worked in the service less than

35 hours per week were excluded from this analysis as was the one counsellor who replied as other

factors might have affected their responses. The final sample of 201 participants was made up of 105

Psychological Wellbeing Practitioners (PWP) and 96 High Intensity (HI) therapists.

Participant Characteristics

Table 1 shows the demographic and job characteristics of the study participants by type of IAPT

practitioner. PWPs tended to be younger than HI therapists and had worked for fewer years in

mental health services prior to and since joining their present IAPT service. They had higher mean

caseloads and more hours of patient contact per week than HI therapists, particularly for patient

contact by telephone. In fact 22/105 PWPs reported 30 or more hours a week in patient contact

(with the maximum being 40) while this applied to only 2/96 HI therapists. PWPs received case

management each week (which generally HI therapists did not) and HI therapists spent more time

supervising than PWPs. Table 1 also shows burnout data by type of practitioner. Emotional

exhaustion, disengagement and burnout were all higher in PWPs than in HI therapists. Because the

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profile of characteristics was so different between PWPs and HI therapists the remaining results are

presented separately for the two groups of practitioners.

[Table 1 around here]

Predictors of emotional exhaustion

Table 1 shows that mean scores for emotional exhaustion scale were higher for PWPs than HI

Therapists. Table 2 shows that stronger agreement that organisational structure and conflicts with

other professionals were a source of pressure at work predicted greater emotional exhaustion and

disengagement for both PWP and HI therapists. Because these latter two ‘predictors’ could plausibly

be considered as consequences of emotional exhaustion and disengagement, they were not

included in the multiple regression models which included only the more objective job

characteristics.

[Tables 2 and 3 around here]

Tables 2 and 3 show that mean levels of emotion exhaustion were lower for male PWPs than

females and gender became a statistically significant predictor in the multiple regression model.

More hours of patient contact, specifically telephone contact, and more hours inputting data and

doing overtime were all associated with higher levels of emotional exhaustion (Table 2) and the

latter two remained in the regression model as independent predictors (Table 3). Among HI

therapists greater total hours of patient contact and particularly face to face and telephone contact

predicted higher levels of emotional exhaustion (Table 2). Total hours of contact and telephone

contact remained as independent predictors in the regression model (Table 3). Unexpectedly,

greater awareness of targets was associated with lower levels of emotional exhaustion when

considered as a single predictor but did not explain a significant amount of variation in emotional

engagement in the multiple regression model.

Predictors of disengagement

PWPs from a Black or Ethnic Minority group had higher mean disengagement than those from the

majority group and this difference became statistically significant in the multiple regression model

(Tables 2 and 3). Longer time in the service, total hours of patient contact, hours inputting data and

hours of overtime all predicted higher levels of disengagement among PWPs (Table 2) and time in

the service and hours of overtime remained in the multiple regression model (Table 3). Hours of

clinical supervision received was associated with lower levels of disengagement among PWPs and

this effect remained in the multiple regression model (Tables 2 and 3). Among HI therapists all the

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indicators of increased patient contact were associated with higher levels of disengagement when

considered as individual predictors (Table 2) and total hours and hours of telephone contact

remained as independent predictors in the multiple regression model. As for emotional exhaustion,

greater awareness of targets was associated with lower levels of disengagement but did not remain

as an independent predictor in the multiple regression model.

Prevalence and Predictors of Burnout

Table 1 shows that 68.6% of PWPs and 50.0% of HI therapists in the study were categorised as

suffering from problematic levels of burnout. The 95% confidence intervals indicate that the likely

range of the true prevalence of burnout is between 58.8% and 77.3% for PWPs and 39.6% and 60.4%

for HI therapists.

[Table 4 around here]

Table 4 shows that for PWPs, significantly higher odds of burnout were predicted by longer duration

in the current IAPT service, higher total hours of patient contact, more hours of face-to-face contact,

more hours of inputting data and more hours of overtime. Hours of supervision received predicted

lower odds of burnout. In the multiple logistic regression model each hour of overtime predicted

higher odds of burnout (Odds ratio [OR] = 1.87, 95% Confidence Interval [CI] 1.27 – 2.77, P=.002) and

each hour of clinical supervision received predicted lower odds of burnout (OR = 0.41, 95%CI .18

- .94, P=.036). There was an interaction between weekly hours of telephone contact and time in the

service (Likelihood Ratio test for interaction p=0.002). Amongst the 53% of PWPs who had worked in

the service for two or more years, each hour of telephone contact predicted higher odds of burnout

(OR = 1.20, 95%CI 1.01 – 1.43) but this was not evident amongst the 47% who had worked in the

service for less than two years (OR = 0.93, 95%CI .65 – 1.33).

A similar process was applied to the data for the HI therapists. However, table 4 shows that all of the

confidence intervals for the odds ratios included the value of 1.0 so there was little evidence that

any of the demographic, experience or job characteristics predicted burnout in this group of IAPT

workers. There was also no evidence of interaction between any of the characteristics and time in

the service.

Discussion

Our data suggest that just over two thirds of PWPs and half of HI therapists in the study were

suffering from problematic levels of burnout. The 95% confidence intervals indicate that the likely

range of the true prevalence of burnout is between 58.8% and 77.3% for PWPs and 40.2% and 60.8%

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for HI therapists. These levels fall within the higher end of the prevalences of 21% to 67% reported in

a recent review among mental health workers (Morse et al., 2012). A study amongst IAPT

practitioners (Steel, Macdonald, Schroder, & Mellor-Clark, 2015) using the MBI did not estimate the

prevalence of burnout but reported higher levels of emotional exhaustion and lower levels of

depersonalisation relative to other practicing psychologists. It cannot be ruled out that the high

prevalences of burnout found in our study were the result of attracting participants who had greater

levels of emotional exhaustion and disengagement than their colleagues. However, the research was

presented as a study of “different aspects of an IAPT worker’s role and their experiences of their

work” in order to avoid such bias. Similarly, the mixture of sampling strategies used for this study is

likely to have resulted in a sample more representative of IAPT practitioners in the South of England

but there is no reason to expect that prevalences of burnout would be higher among workers in that

geographical area. The high prevalences of burnout found are a concern because if representative

they have serious implications for patient care and the health of the practitioners themselves. In

addition burnout has consistently been associated with higher levels of staff turnover. Retention of

PWPs has been highlighted as a problem for IAPT services (Centre for Outcomes Research and

Effectiveness, 2013) and while there are a number of plausible explanations, our data suggests that

burnout could be an important factor. Further research is needed to better understand the extent

and consequences of burnout in this group.

Our results are generally consistent with other researchers’ assertion (Morse et al., 2012) that it is

organisational-environmental variables rather than characteristics of the workforce which more

strongly predict burnout and its constituents of emotional exhaustion and disengagement. However,

there was some indication that men had lower levels of emotional exhaustion than women and this

is consistent with the findings from a recent meta-analysis (Purvanova & Muros, 2010). The meta-

analysis also found that men had higher levels of depersonalisation but we did not use this measure

in our study and we found no evidence that disengagement was higher in men. Having a

black/minority ethnic (BME) background was associated with higher levels of disengagement in our

study. Higher levels of depersonalisation have been found amongst African-American relative to

Caucasian-American childcare professionals in the United States (Evans, Bryant, Owens, & Koukos,

2004) but possible associations between ethnicity and burnout generally appears to be an under-

researched area.

The analysis of job characteristics as predictors provides some explanation as to what might be

leading to the high prevalences of burnout among the IAPT practitioners in our study. More hours of

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patient contact predicted higher levels of emotional exhaustion and disengagement for both types

of practitioner in the unadjusted analysis and remained as an independent predictor for the HI

therapists in the multiple regression models. There are indications that telephone contact with

patients is a particularly important factor associated with burnout. In the multiple regression models

greater hours of telephone contact predicted higher odds of burnout among PWPs who had worked

in the service for two or more years as well as independently predicting higher levels of emotional

exhaustion and disengagement among HI therapists. The type of telephone contact with patients is

different for PWPs and HI therapists. For HI therapists it is likely to be solely for organising

appointments or following up missed appointments etc. whereas for PWPs in addition to telephone

contact for administrative work it is used extensively for therapeutic work. For PWPs hours of

overtime, indicating a generally higher workload, were predictive of emotional exhaustion,

disengagement and burnout. Record keeping is a particular feature of IAPT and more hours of

inputting data independently predicted higher levels of emotional exhaustion amongst PWPs.

Burnout is a phenomenon that develops over time in a stressful environment and longer time in the

current IAPT service predicted higher levels of disengagement among PWPs. Another feature of IAPT

work is meeting targets in terms of patient recovery and it was expected that awareness of targets

would lead to increased burnout. However, effect estimates suggested that awareness of targets

was generally associated with lower emotional exhaustion, disengagement and burnout.

Feeling under pressure due to organisational structure and due to colleague relationships was

strongly associated with emotional exhaustion, disengagement and burnout for both types of

practitioner. While the direction of causation among these variables is not clear, the mixture of

emotional exhaustion, disengagement and burnout and negative feelings towards the organisation

and colleagues is clearly not a state conducive to quality patient care, staff well-being or retention.

These results suggest interventions which might reduce emotional exhaustion, disengagement and

burnout in IAPT practitioners. The consistency of workload related predictors suggests that

workloads need to be managed differently in order to avoid high numbers of contact hours

(particularly by telephone) and to keep overtime to a minimum. In this study a fifth of PWPs

reported 30 or more hours of patient contact per week (with the maximum being 40 hours) while

only 2% of HI therapists had this amount of patient contact (and the maximum was 32 hours). If

burnout is part of the explanation for the poor retention of PWPs then the increased costs of

reducing workloads might be offset by cost savings due to better staff retention and less sick leave. It

would be interesting to consider these aspects together in a cost-benefit analysis in future research.

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Another approach to reducing burnout suggested by our results is to increase the amount of clinical

supervision received. Among PWPs more hours of clinical supervision received predicted lower levels

of disengagement and burnout. This pattern of results is consistent with the job demands-resources

model which proposes that job resources – in this case clinical supervision - may be able to offset or

delay emotional exhaustion progressing to disengagement and burnout (Demerouti et al., 2001).

Further research is needed to clarify the relationship between supervision, disengagement and

burnout.

Conclusion

In this study, estimated levels of burnout in IAPT workers were among the highest seen in the

mental health workforce. Best estimates are that over two thirds of PWPs and half of HI therapists

were suffering levels of emotional exhaustion and disengagement suggestive of clinically

problematic burnout. Higher workloads, indicated by more hours of overtime and more hours of

telephone contact with patients were the most consistent predictors of higher odds of burnout and

its constituents. Receiving more hours of supervision was associated with lower levels of

disengagement and burnout. There was also some indication that males had lower levels of

emotional exhaustion than females and that BME workers had higher levels of disengagement than

non-BME staff. Strategies to manage workload and increase clinical supervision appear to be the

most promising interventions to reduce burnout.

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Table 1 Participant characteristics by type of IAPT practitioner Participant characteristics

Mean (sd) or % (n)PWP

N=105HI

N=96p (differencea)

Demographic characteristics% male 14.3% (15) 22.9% (22) .115

% aged 40+ 19.0% (20) 50.0% (48) <.001Age 32 (9.3) 40 (9.0) <.001% Black/Minority ethnic group 17.1% (18) 13.5% (13) .480Mental health work experience (years)Prior to current IAPT service 3.6 (3.93) 10.1 (7.11) <.001Current IAPT service% Current IAPT service ≥ 2 years

1.8 (1.12)53.3% (56)

2.5 (0.95)82.3% (79)

<.001<.001

Job characteristics (per week)Hours providing clinical supervision 0.1 (0.46) 0.9 (1.6) <.001Hours providing case management 0.5 (1.3) 0.5 (1.0) .738Caseload (number of patients) 41.4 (19.6) 21.6 (7.6) <.001

Hours of patient contact 22.2 (8.39) 18.8 (5.70) .001 Face-to-face 10.7 (8.18) 17.3 (4.84) <.001 By telephone 10.0 (8.4) 1.2 (1.51) <.001 In groups 1.8 (1.55) 1.1 (3.60) .069Hours inputting data 9.8 (4.65) 10.0 (4.67) .762Hours of overtime 2.2 (2.11) 2.6 (2.90) .304Does not have own desk 42.9% (45) 55.2% (53) .080

Awareness of targets b 3 4 .037Hours clinical supervision received 0.8 (0.41) 0.9 (0.57)c .705Hours case management received 1.0 (0.48) - -Outcome variablesEmotional Exhaustion 2.64 (0.56) 2.49 (0.50) .051Disengagement 2.44 (0.54) 2.23 (0.50) .003% categorised as suffering burnout 68.6 (72) 50.0 (48) .007

a From t-test comparing 2 means except for categorical data where it is from a Χ2 test and ordinal data where it is from a Mann-Whitney U Test.b For Awareness of targets which is ordinal with scores from 1 to 5 (5 indicating most awareness) the median is presentedc N=95 for HI practitioners due to one missing data point

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Table 2 Individual predictors of Emotional Exhaustion and Disengagement for each type of IAPT practitioner Emotional Exhaustion Disengagement

PWP HI Therapists PWP HI TherapistsBa P Ba p Ba p Ba p

Demographic characteristicsMale -1.97 .133 -.86 .380 -.01 .993 .49 .611Age .04 .392 (.560b) -.04 .389 (.491 b) .08 .079 (.234 b) -.07 .121 (.260 b)Black/Minority Ethnic group (BME) .82 .500 2.1 .077 1.49 .185 1.02 .386Mental health work experience (years)Prior to current IAPT service .16 .160 -.03 .667 .25 .019 -.04 .461Current IAPT service .74 .066 .16 .722 1.58 <.001 .26 .541Job characteristicsHours providing case management or supervision

-.01 .968 -.16 .432 -.01 .979 -.31 .113

Caseload (number of patients) .02 .303 .06 .279 .04 .095 .14 .008Hours of patient contact .18 .001 .20 .005 .15 .003 .23 .001 Face-to-face .05 .386 .22 .011 .06 .227 .24 .003 By telephone .11 .050 .75 .005 .06 .224 .79 .003 In groups -.01 .960 .20 .074 -.10 .731 .25 .024Hours inputting data .29 .003 .07 .433 .18 .051 .02 .809Hours of overtime .92 <.001 .24 .092 .71 <.001 .10 .475Does not have own desk -1.04 .260 .90 .279 -.29 .737 -.27 .738Greater awareness of targets -.63 .111 -.82 .029 -.25 .502 -.81 .028Hours supervision receivedc -1.24 .123 -.61 .527 -1.97 .007 -.85 .370Hours case management received -.19 .847 - - 0.20 .827 - -Feels under pressure due to:Organisational structure and processes 2.90 <.001 2.21 <.001 3.01 <.001 2.65 <.001Relationships and conflicts with other professionals

3.53 <.001 2.24 .002 3.26 <.001 2.39 .001

a Unstandardised regression coefficients. For males this is the mean difference from females and for BME the mean difference from White British.b P-value for age considered as a categorical variable with age-groups being 20-29, 30-39, 40-49, 50-63 in case of non-linearityc N=95 for HI Therapists due to one missing data point

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Table 3 Independent predictors of emotional exhaustion and disengagement for each type of IAPT practitioner

Predictor Emotional Exhaustion

Disengagement

B a p B a pPWPs Male -2.81 .016(N=105) BME 2.01 .034

Time in current service 1.38 <.001Hours inputting data per week .24 .006Hours of overtime per week .90 <.001 .62 <.001Hours of supervision received per week -1.83 .004

R2 .27 .32

HI therapists

Hours of patient contact per week .16 .032 .18 .010

(N=96) Hours of telephone contact per week .57 .035 .59 .027

R2 .13 .16a Unstandardised regression coefficients. For males this is the mean difference from females and for BME the mean difference from White British.

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Table 4 Individual predictors of burnout from logistic regression modelling for each type of IAPT practitioner PWPs (N=105) HI therapists (N=96)

% burnout (r/n)b

Odds Ratio

95% CI for OR p a % burnout (r/n) b

Odds Ratio

95% CI for OR p a

Demographic characteristics

Gender Female 71.1 (64/90) 1 51.3 (38/74) 1 Male 53.3 (8/15) 0.46 .15 – 1.41 .181 45.4 (10/22) 0.79 .30 – 2.05 .628Age <40 69.4 (59/85) 1 58.3 (28/48) 1 40+Ethnicity

65.0 (13/20) 0.82 .29 – 2.89 .704 41.7 (20/48) 0.51 .23 – 1.15 .104

White 67.8 (59/87) 1 50.6 (42/83) 1 Black/Minority ethnic group 72.2 (13/18) 1.23 .40 – 3.80 .712 46.2 (6/13) 0.84 .26 – 2.70 .766

Mental health work experience (years)

Prior to current IAPT service 1.00 .90 – 1.12 .933 0.97 .91 – 1.02 .264Current IAPT service 1.58 1.06 – 2.34 .020 1.26 .88 – 1.77 .288Job characteristicsHours providing supervision or case management 0.99 .77 – 1.26 .916 1.01 .83 – 1.23 .901Caseload (number of patients) 1.01 .99 – 1.03 .408 1.00 .95 – 1.06 .915

Hours of patient contact 1.08 1.02 – 1.14 .003 1.03 .96 – 1.11 .408 Face-to-face 1.06 1.00 – 1.12 .046 1.02 .94 – 1.11 .656 By telephone 1.02 .97 – 1.07 .481 1.23 .91 – 1.65 .173 In groups 0.91 .70 – 1.18 .484 1.02 .91 – 1.14 .714Hours inputting data 1.12 1.01 – 1.24 .019 0.97 .89 – 1.06 .516Hours of overtime 1.60 1.20 – 2.12 <.001 1.01 .88 – 1 16 .860Does not have own desk 0.86 .37 – 1.97 .716 1.81 .80 – 4.01 .152Greater awareness of targets 0.84 .58 – 1.20 .331 0.78 .53 – 1.13 .193Hours supervision receivedc 0.44 .20 –.94 .034 0.90 .36 – 2.29 .832Hours case management received 1.65 .59 – 4.64 .337

Feels under pressure due to:

Organisational structure and processes 3.53 1.68 – 7.43 .001 2.42 1.23 – 4.78 .011Relationships and conflicts with other professionals 5.52 1.19 – 10.5 .023 2.16 .96 – 4.83 .062

a From likelihood ratio testsb r/n indicates numerator over denominator for proportionsc N=95 for HI therapists due to one missing data point

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