Demands, control, supportive relationships and well-being amongst British mental health workers

ORIGINAL PAPER

Demands, control, supportive relationships and well-beingamongst British mental health workers

Stephen Wood • Chris Stride • Kate Threapleton •

Elizabeth Wearn • Fiona Nolan • David Osborn •

Moli Paul • Sonia Johnson

Received: 26 October 2009 / Accepted: 23 June 2010 / Published online: 16 July 2010

� Springer-Verlag 2010

Abstract

Purpose Staff well-being is considered to be a potential

problem within mental health occupations, and its vari-

ability is in need of investigation. Our starting point is to

assess the role of demands, control and supportive rela-

tionships that are at the core of Karasek’s model. The study

aims to assess the relationship amongst mental health

workers of job demands, control and support (from peers

and superiors) with multiple measures of well-being.

Method Data were obtained through a self-completion

questionnaire from mental health staff in 100 inpatient

wards, 18 crisis resolution/home treatment teams and 18

community mental health teams. The data was analysed

using multilevel regression analysis.

Results Job demands (negatively), control (positively)

and supportive relationships (positively) are each uniquely

associated with the five measures of well-being included in

the study: namely intrinsic satisfaction, anxiety, depres-

sion, emotional exhaustion and personal accomplishment.

Non-linear and interaction effects involving these

demands, control and supportive relationships are found,

but vary in type and strength across well-being measures.

Conclusions The combination of low levels of demands

and high levels of control and supportive relationships is

good for the well-being of mental health staff. Our results

suggest that management initiatives in mental health ser-

vices should be targeted at creating this combination within

the working environment, and particularly at increasing

levels of job control.

Keywords Well-being � Job satisfaction �Work demands � Job control � Karasek

Background

Staff well-being has figured prominently in the discussion

of the effectiveness of mental health services. It is widely

accepted that the well-being of staff and their associated

morale are vital for both ensuring a reliable and cost-

effective service and reaping benefits from investments in

training, new management methods, innovative service

models, and initiatives intended to improve quality and

safety. Problems within mental health services such

as safety issues, inadequate resources, large caseloads,

excessive administrative work and constraints on imple-

menting novel therapies, with the attendant overreliance

on medication, are seen as sources of low morale and

high stress levels [11, 23–25]. Equally, low levels of staff

S. Wood (&)

School of Management, University of Leicester,

Leicester LE1 7RH, UK

e-mail: [email protected]

C. Stride

Institute of Work Psychology, University of Sheffield,

Sheffield S10 2TN, UK

K. Threapleton

Division of Rehabilitation and Ageing,

University of Nottingham, Nottingham NG7 2UH, UK

E. Wearn � D. Osborn � S. Johnson

Research Department of Mental Health Sciences,

University College London, London W1W 7EJ, UK

F. Nolan

Centre for Outcomes Research and Effectiveness,

University College London, London WC1E 7HB, UK

M. Paul

Health Sciences Research Institute, University of Warwick,

Coventry CV4 7AL, UK

123

Soc Psychiatry Psychiatr Epidemiol (2011) 46:1055–1068

DOI 10.1007/s00127-010-0263-6

well-being and satisfaction are often implicated in specific

problems facing mental health inpatient services in the

British National Health Service (NHS), such as insufficient

and limited patient–staff contact, negative experiences of

the hospital environment, worries about safety among

inpatients, and high rates of labour turnover, vacancy and

sickness [7, 21]. Given such diagnoses, investment in

teams and staff were at the heart of the government’s

modernisation agenda to improve mental health services

and the quality of care during the past decade [5, 6].

Against this background, the National Institute of Health

Research Service Delivery and Organisation (SDO) pro-

gramme funded a systematic review of literature by Cahill

et al. [2] on staff morale within inpatient mental health

units. This concluded that, on the basis of the research so

far, it was not possible to give an accurate picture of staff

morale. Burnout, job satisfaction and overall psychological

well-being were the principal measures of morale used in

the studies and the available evidence suggested that job

satisfaction was high and burnout at moderate levels. The

studies often had very small samples, and many were in

single sites. It did not appear from Cahill et al.’s review [2]

that any diversity in the results was systematically related

to the measures used; the overall conclusion was that high-

quality research on staff morale and well-being in mental

health was lacking in this area, and the evidence base on

the antecedents of well-being was weak. Although the

report centred on inpatient services, these arguments apply

equally to other mental health services, such as community

mental health teams where there has been less research.

In light of this, the SDO commissioned a research pro-

ject to investigate a range of factors that might impact upon

well-being using a variety of measures applicable to all

types of mental health service provision. This paper reports

the part of this study examining the effect of three key job

or organisational characteristics: the influence of the degree

of control individual staff members have in their work, the

extent of the demands placed on them, and the degree of

support they receive to do their job. This triad has been at

the centre of well-being theory within work psychology for

the past two decades, particularly following Karasek’s

demand and control model of work-related stress [15, 16],

and extensions of it that include support from immediate

line managers or colleagues.

Whilst several recent research papers have suggested

that portrayals of a crisis in staff morale in Britain may be

exaggerated [1], they continue to show that there is con-

siderable variation in staff well-being and thus we need to

investigate what explains this variability. The trinity of

demands, control and support provides a good starting

point for an assessment of the factors affecting staff well-

being amongst mental health workers. Some of the factors

commonly associated with mental health settings that have

been linked to low staff well-being—such as excessive

administrative duties, shift working, high labour turnover,

acutely ill and uncooperative patients, violent incidents and

drug use on the wards, high patient turnover, and con-

straints on creating an adequate therapeutic environment—

may be subsumed under the more general factors, e.g. high

demands, low control or managerial support. We can then

assess the relevance of constructs that cannot be subsumed

under these more generic headings by estimating the

additional explanatory power they add relative to Karasek’s

triad of concepts. Similarly, the Karasek model can be used as

a benchmark for assessing the role of other non-mental

health-specific factors, including recent managerial initia-

tives such as the introduction of appraisal or mentoring

systems, or more longstanding ones such as training and

development. This paper thus presents the first study that

systematically tests the applicability of Karasek’s model to

a range of mental health staff (though Tummers et al. [28]

compared mental health nurses with general nurses in the

Netherlands using the Karasek framework).

Research studies, both prior and subsequent to the Cahill

et al. report, have infrequently included one or more of

support, demands and control, but the range of antecedents

to well-being explored has remained wide, encompassing a

mixture of mental health-specific measures (e.g. the threat

of violence from acutely ill patients), and general factors

(e.g. good pay, task clarity). Cahill et al.’s study [2, p. 58]

concluded that it was not possible to determine which

factors are most likely to increase levels of satisfaction and

morale of mental health workers on the basis of the existing

studies of inpatient care, though workloads, job charac-

teristics and social support dominated the lists of potential

determinants suggested by the evidence so far.

Reid et al. [25], in one of the few qualitative studies

comparing community and ward staff, indicated that lack of

autonomy was a source of dissatisfaction amongst ward staff,

whereas the excessive demands associated with client care

were more significant for community staff. Edwards et al.

[9], reviewing evidence from outside the UK, included high

autonomy (in decision-making), co-worker support and

good supervision (which we can take to mean supportive

management) amongst significant factors explaining low

levels of stress. Demands or workload, autonomy and col-

league support were also included in Michie and Williams’

[20] review of morale amongst health workers.

Theory

Support, control and demands

Karasek’s [15] theory is centred on the notion that psy-

chological strain results from the demands of a work

1056 Soc Psychiatry Psychiatr Epidemiol (2011) 46:1055–1068

123

situation and the range of decision-making freedom avail-

able to the worker facing those demands. His model thus

identifies job characteristics as the principal source of dis-

tress in the workplace, since it proposes that psychological

strain is caused by the combination of high job demands and

low job control (for this reason it is also called the

demands–control model). The underlying rationale of the

model is that workers experience distress when this com-

bination of circumstances exists because they are prevented

from formulating effective responses to deal with the

challenges of the job. Conversely, low demands and high

control are associated with high levels of well-being.

The importance of supportive relationships in organisa-

tions is a recurring theme in work psychology; the

increasing salience given to bullying illustrates the renewed

concern about extreme forms of non-supportive behaviour.

Payne [22, see also 14 and 18], in particular added the

concept of support to the demand and control model, and

suggested that support, particularly when interpersonal,

could reduce the level of adaptive energy needed to cope

with high demands under conditions of low control.

Karasek (along with Theorell [17, pp. 68–76]) incorporated

such thinking into his model so that social support buffers or

protects the individual against the worse effects of strain;

hence, social support and decision latitude buffer the

adverse effects of high job demands. Such social support

manifests itself in a variety of ways. For example, it may

help people manage their feelings better so they resolve

problems more easily, or offer motivation so that they are

reassured that extra effort or persistence with a problem will

pay off [31]. It may also contribute positively to role clarity.

This demand–control–support model for well-being can

be formulated in two ways: initially in an additive form,

and then extended to an interactive form. The additive form

states that high demands, low control and low social sup-

port each cause psychological strain: i.e. the unique

(independent) effects of each of the three constructs have a

significant impact upon strain. For example, supportive

management or colleagues have a beneficial effect irre-

spective of the whether people are facing stressful demands

or have limited control.

The interactive form of the demand–control–support

model further predicts that control and social support

buffer the negative impact of high demands on well-being

(i.e. they interact with demands to reduce its negative

impact). Under both the additive or interactive forms of the

model, we would expect that psychological strain will be

greatest given the combination of high demands with low

control and low social support (in the demand–control–

support model), or conversely that well-being will be

greatest when employees have low demands, high control

and high support. The Karasek model can therefore be

tested in two ways: initially by examining whether job

demands, job controls and support independently predict

well-being, and then by assessing whether there is a further

interactive (i.e. multiplicative) relationship between them.

A further consideration when probing this model is the

possibility of non-linear effects of job demands or job

control upon well-being. For instance, just as high demands

may be overwhelming, or ‘‘toxic’’ to use Warr’s [31,

p. 181] word, low demands may also be so unchallenging

as to create feelings of frustration and monotony. Likewise,

just as high control may prove beneficial to well-being,

very low control may act positively in freeing employees

from a sense of responsibility. And, though increasing

support initially benefits well-being, there may come a

point where extra amounts of support offer no further

benefit.

A review by de Lange et al. [4] of research testing the

Karasek model revealed that just below half of the pub-

lished studies provide support for the additive version, and

that the corroboration does not vary with the quality of the

study. It also concluded that additional interactive rela-

tionships are rare. Another review confined to the longi-

tudinal studies, Van der Doef and Maes [29] showed there

was considerable support from these for the additive

model, but again—partly because it is infrequently tested,

and no doubt also often under-powered and subject to a

greater debilitating effect of measurement error—less

convincing evidence for the interactive model. These

reviewers concluded that the use of specific measures of

control that correspond directly to the demands in the jobs

being studied is more likely to yield results that support the

buffering role of control. Subsequent studies with more

focused measures have also failed to find strong interaction

affects, and the purely additive model looks the more

robust [31, p. 205]. Nonetheless, a general survey of

employees in Britain using a two-item measure of demands

did offer support for the interactive demands and control

model of job satisfaction and contentment–anxiety [33].

The concept of well-being

Previous investigations of Karasek’s model have tended to

focus on just one or two measures of well-being, and the

range of different measures applied across studies is quite

broad. The review of such work as of the late 1990s by Van

der Doef and Maes [29] showed that job satisfaction and, to

a lesser extent, depression and anxiety have been the most

widely used well-being outcomes.

In work psychology well-being is a multi-dimensional

construct. Warr [30, 31] conceptualises job-related

well-being in terms of three dimensions: dissatisfaction to

satisfaction, anxiety to contentment (or comfort), and

depression to enthusiasm. His model of well-being is based

on what is known as the circumplex model of affect [27],

Soc Psychiatry Psychiatr Epidemiol (2011) 46:1055–1068 1057

123

that describes it in terms of two orthogonal dimensions of

pleasure and arousal, derived from the more general

models of emotions of Watson and Tellegren [32] and

others [for instance 10, 26]. Pleasure relates to emotional

feelings about whether one is feeling good or bad about

one’s job or aspects of it. As such, it is independent of

arousal, since arousal may provoke positive or negative

feelings. Mental arousal ranges from activation to deacti-

vation and includes varying states, from feeling alert to

sluggish, calm to tense, contented to anxious, depressed to

enthusiastic.

Positive ends of the continuum in both the anxiety–

contentment and depression–enthusiasm dimensions are

identified by a state of high pleasure or positive affect. But

their negative ends are differentially related to arousal.

Anxiety entails high arousal and depression entails low

arousal. The traditional emphasis on job satisfaction mea-

sures only the pleasure dimension, the extent of pleasure

that one gains from one’s job.

Equally, Maslach’s [19] concept of burnout has been

used in several tests of the Karasek model—for example, in

a sample of social workers and construction workers—and

much of the research on mental health staff [1]. It is seen as

especially applicable to the caring professions [19]. The

overall term refers to the experience of long-term exhaus-

tion and diminished interest, but Maslach identified three

dimensions. The core factor is that of emotional exhaus-

tion, which ‘‘refers to feelings of being emotionally over-

extended and depleted of one’s emotional resources’’ [19,

p. 69]. The measures of it in Maslach’s inventory cover

tension, anxiety and other factors that are mainly, but not

exclusively, related to the anxiety–contentment dimension

in Warr’s terms. The other two dimensions of burnout are a

reduced sense of personal accomplishment and deperson-

alisation. Personal accomplishment refers to feelings of

low self-efficacy and competency, and is associated nega-

tively with depression and positively with active partici-

pation in job-related decision-making; hence, it is related

to the depression–enthusiasm dimension in Warr’s terms.

Depersonalisation refers to the extent to which workers

become distant from and cynical about their work. In the

case of human service work, it is particularly reflected in

the extent to which workers are critical of their clients or

customers.

Methodology

Our study aims to assess the relationship amongst mental

health workers of job demands, control and support (from

peers or superiors) with well-being, using multiple mea-

sures of well-being. Multicentre research ethics approval

was obtained from the Brighton and Mid-Sussex Research

Ethics Committee and research governance approval

obtained from each participating NHS Trust.

Study design, sampling and data collection

Data reported in this paper are derived from the Inpatient

Staff Morale Study, funded by the SDO programme to

inform policy and service planning regarding the mental

health inpatient workforce. Subsequent papers will

describe overall levels of morale measured by various

indicators, explore relationships between indicators, and

test the effects of adding variables describing built envi-

ronment, ward organisation, area and service-user demo-

graphics, and adverse incidents to the demand–support–

control model. This study consists of a large sample of

mental health staff working in psychiatric wards, together

with smaller comparison samples of community mental

health teams for adults of working age and staff in crisis

resolution teams (teams available throughout England for

short-term assessment and management of crises, also

known as crisis and home treatment teams, intensive home

treatment teams or crisis assessment teams). The study

covers all occupational groups, full and part-time workers,

and qualified and unqualified workers. Managers, however,

are excluded from the analysis sample within this paper as

our measure of managerial support is based on workers’

assessment of them.

The sample was recruited from 19 NHS Trusts and,

within these, from 100 inpatient wards, 18 community

mental health teams and 18 crisis resolution and home

treatment teams. The inpatient sample was taken from 50

acute wards, 10 rehabilitation wards, 10 forensic wards, 10

mental health care of older people wards, 10 child and

adolescent mental health services (CAMHS) wards and 10

psychiatric intensive care units (PICUs). We will use the

term service units to refer collectively to the inpatient

wards and outpatient services.

Trusts were selected on the basis of the proximity to the

four universities involved in the research team (University

College London, Sheffield, Warwick and Bristol) and to

hubs of the Mental Health Research Network, which

greatly facilitated access as anticipated. We also purpo-

sively selected Trusts with wards covering a wide range of

(a) geographical and socio-demographic characteristics

(including some with close links to academic centres) and

(b) mental health sub-specialties.

Our self-completion questionnaire was distributed to all

workers within the 100 wards and 36 outpatient service

teams directly to staff or their manager by a member of the

research team. The completed questionnaires were returned

via post to a member of the research team. Questionnaire

distribution was preceded by a presentation of the research

at ward or team meetings for both the day and night shifts.


123

Follow-up visits were made by a member of the research

team to stimulate responses. Ward or team managers were

also encouraged to rally staff to complete the

questionnaires.

Each service unit’s number of responses varied from 4

to 40, with an average of 14 employees. Out of a total of

3,545 people who received the questionnaire, 2,258 people

responded, yielding a response rate of 63.7%. Within the

wards, the response rate varied from 21.95 to 100% with a

median rate of 62.28%. Within the Trusts, the response rate

varied from 51.91 to 71.75% with a median rate of 60%.

Measures

We describe only those study measures on which the paper

reports.

Well-being outcomes

Six distinct measures of work-related well-being were

included in the questionnaire: intrinsic satisfaction, anxi-

ety, depression, emotional exhaustion, personal accom-

plishment and depersonalisation.

Intrinsic satisfaction was measured by a five-item scale

based on asking respondents how satisfied they were with

the following aspects of their job: ‘‘the sense of achieve-

ment I get from my work’’, ‘‘the scope for using my own

initiative’’, ‘‘the amount of influence I have over my job’’,

‘‘my involvement in decision making’’, ‘‘the opportunities

that I have to use my abilities’’. The first four of these items

were taken from the 2004 Workplace Employment Rela-

tions Survey, the last from the NHS National Staff Survey

of 2006. Respondents rated their satisfaction with each of

these facets on a five-point scale: 5 = ‘‘very satisfied’’,

4 = ‘‘satisfied’’, 3 = ‘‘neither satisfied nor dissatisfied’’,

2 = ‘‘very dissatisfied’’, or 1 = ‘‘dissatisfied’’.

Complementary three-item measures of anxiety and

depression [30] were employed, and respondents were

asked, ‘‘Thinking of the past few weeks, how much of the

time has your job made you feel’’ each of six negative

states: tense, uneasy, worried (for anxiety), miserable,

depressed and gloomy (for depression). For both scales, the

response categories were coded as 1 = ‘‘never’’, 2 =

‘‘occasionally’’, 3 = ‘‘some of the time’’, 4 = ‘‘most of the

time’’, 5 = ‘‘all of the time’’.

Maslach’s [19] concept of burnout is examined by 22

items formed by three subscales: emotional exhaustion,

personal accomplishment and depersonalisation. Levels of

emotional exhaustion are measured by a nine-item subscale

based on asking how often the respondent feels the fol-

lowing states: ‘‘emotionally drained from my work’’, ‘‘used

up at the end of the working day’’, ‘‘fatigued when I get up

in the morning’’, ‘‘burned out from my work’’, ‘‘frustrated

by my job’’, ‘‘like I’m at the end of my tether’’, ‘‘working

too hard on the job’’, ‘‘working with people involves too

much stress’’, and ‘‘working with people all day is a

strain’’. Personal accomplishment is similarly designed as

an eight-item subscale, asking about the extent to which the

respondent: ‘‘can easily understand patients’ feelings’’,

‘‘deals effectively with the patients’ problems’’, ‘‘positively

influences people’s lives’’, ‘‘feels very energetic’’, ‘‘can

easily create a relaxed atmosphere’’, ‘‘feels exhilarated

after working with patients’’, ‘‘has accomplished worth-

while things in job’’, and ‘‘deals with emotional problems

calmly’’. Finally, the depersonalisation subscale consists of

five items: respondents were asked the extent to which they

‘‘treat patients as impersonal objects’’, ‘‘become more

callous toward people’’, ‘‘worry that the job is hardening

emotionally’’, ‘‘don’t really care what happens to patients’’,

and ‘‘feel patients blame me for their problems’’. For all

three subscales, the response coding ranges from

0 = ‘‘never’’ to 6 = ‘‘everyday’’, with 3 = ‘‘a few times a

month’’ as the mid-point.

Hypothesised work characteristics antecedents

The measures of the principal independent variables were

based on those designed by Haynes et al. [12] to be

applicable to health workers.

Demand was measured by seven items asking respondents

how often they met each of the following problems in car-

rying out their work: ‘‘I don’t often have enough time to carry

out my work’’, ‘‘I cannot meet all the conflicting demands

made on my time’’, ‘‘I never finish work completing every-

thing I should have’’, ‘‘I am asked to do work without ade-

quate resources to complete it’’, ‘‘I cannot follow best

practice in the time available’’, ‘‘I am asked to do basic tasks

stopping me completing more important ones’’, and ‘‘vary-

ing levels of demands on my time’’. Respondents answered

the question on a five-point response coding, ranging from

1 = ‘‘not at all’’ to 5 = ‘‘a great deal’’, with 3 = ‘‘moderate

amount’’ as the mid-point.

Control was again measured by Haynes et al.’s [12] six-

item measure of this construct; the questions asked were to

what extent the respondent could ‘‘determine the methods

and procedures I use in my work’’, ‘‘choose what work I

will carry out’’, ‘‘decide when to take a break’’, ‘‘vary how

I do my work’’, ‘‘plan my own work’’, and ‘‘carry out my

own work in the way I think best’’. In addition, an item that

we designed on patient interaction was included: ‘‘To what

extent do you choose how you interact with patients?’’,

giving a seven-item scale. The response coding employed

was the same as that for demand.

Ward or team manager support was based on three

questions about the extent to which the individual can


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count on their ward or team manager to ‘‘listen when I need

to talk about problems at work’’, ‘‘help me with a difficult

task’’, and ‘‘provide effective leadership for the ward or

team’’. The five-point response coding used was 1 = ‘‘not

at all’’ and 5 = ‘‘completely’’, with 3 = ‘‘to a moderate

extent’’ as the mid-point.

Colleague support was measured by a four-item scale

based on the degree to which the individual can count on

colleagues to ‘‘listen when I need to talk about problems at

work’’, ‘‘help me with a difficult task’’, ‘‘back me up at

work’’, and ‘‘help me in a crisis situation at work, even

though they would have to go out of their way to do so’’.

The response categories were as for manager support.

Control variables

We also collected data on a number of potentially con-

founding demographic variables, namely each respondent’s

age, gender, marital status (coded as single, married or co-

habiting, and divorced, separated or widowed), ethnic

origin (coded as white, Asian, African or Caribbean, and

mixed or other ethnic group), and number of dependants.

Further work-related control variables that were collected

and examined were a respondent’s occupational group

(mental health nurse, social worker, nursing assistant,

occupational therapist, psychiatrist, clinical psychologist,

or mental health nurse who also fulfilled a further role),

their length of service in the service unit and in the mental

health service, employment status (permanent, second-

ment, fixed-term contract, and locum, bank or agency),

the Trust they worked for and the type of ward or team

they worked in (acute, crisis teams, PICU, CAMHS, or

forensic).

Analysis

Our analyses comprised three stages. First, we assessed the

validity and reliability of our proposed measures via a

combination of exploratory factor analysis, confirmatory

factor analysis and reliability analysis. Having derived

valid and internally consistent measures from our ques-

tionnaire items, we then created mean scores across each

scale to give us a single measure of each antecedent and

outcome construct of interest. Secondly, we explored the

sample characteristics and the bivariate relationships

between our measures of support, demands, control and

well-being.

The third stage involved building models to test the

additive and interactive models for the effects of support,

demands and control upon each outcome in turn. Due to

the three-level structure of our data, with employees

nested within services, which in turn were nested within

Trusts, and the subsequent potential to model and explain

random variation at each of the employee, service unit

and Trust levels, we used multilevel regression analysis to

build models for each outcome. This also gave us the

means to test for variation by service unit or Trust in the

effects of employee’s support, demands and control upon

well-being.

For each outcome, sets of variables were entered hier-

archically. Having first partitioned the variance into within-

and between-service unit components, the proposed control

variables were entered as predictors, with those exhibiting

significant effects upon at least one outcome retained for

the further steps of the modelling process. These steps were

first a test of the additive model, via the simultaneous

assessment of the main linear effects of demands, control

and support, and the investigation of potential curvilinear

effects. Following this, the interactions between demands,

control and support were added to test the interactive

version of the Karasek model.

Our analyses were carried out on a maximum working

or ‘analysis’ sample of 1,870 employees from 136 service

units located within 17 different Trusts. This sample was

denuded slightly for each analysis performed due to low

levels of attritional missing data across the study variables

and the subsequent listwise deletion of cases. Given this

large sample size at the employee level, all analyses testing

employee-level effects utilised the p \ 0.005 level of

statistical significance.

Results

Stage 1

To test the validity of the measurement model posited to

underlie the well-being, support, control and demands

constructs, a confirmatory factor analysis was initially run

on the whole sample, with items grouping together to load

onto 10 factors as per their 10 respective scales. This

resulted in a less than adequate fit to the data (chi-square =

6,893 on 1,332 df; with fit indices of CFI = 0.890,

RMSEA = 0.052 and SRMR = 0.060) according to the

widely accepted standards outlined in Hu and Bentler [13].

Furthermore, 6 of the 54 items exhibited particularly weak

communalities with R-squared statistics (the proportion of

their variance explained by the model) of less than 0.30.

In an attempt to find the optimal measurement model for

these items and constructs, we then split the data into

random halves, enabling us to create and test a putative

model on different subsamples whilst sidestepping the

danger of an upward bias on measures of fit caused by

building and testing a model on the same set of data. An

exploratory factor analysis was performed on one half of


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the data, using principal axis factoring as the extraction

method, and assessing the number of factors to be extracted

by a combination of Kaiser’s criterion and Cattell’s scree

plot method, as recommended by Conway and Huffcutt [3].

Oblique rotation was carried out to aid interpretation; the

results suggested the removal of a handful of low-loading

or cross-loading items, specifically ‘‘to what extent do you

choose how you interact with patients?’’ from the job

control scale, ‘‘working too hard on the job’’, ‘‘working

with people involves too much stress’’ and ‘‘working with

people all day is a strain’’ from emotional exhaustion, and

‘‘can easily understand patients’ feelings’’ and ‘‘feel very

energetic’’ from personal accomplishment. Further inves-

tigation of the item frequencies indicated that four of the

five measuring depersonalisation had extremely limited

variability in scores across the sample, with between 70

and 90% of respondents reporting no or very low levels of

depersonalisation; hence, we chose to exclude this entire

subscale.

This resulted in a nine-factor measurement model for the

remaining 43 items, which exhibited an improved fit to the

validation half of the data when tested using a confirmatory

factor analysis; the recommended fit indices were all

enhanced, with CFI = 0.925, RMSEA = 0.051 and

SRMR = 0.050. Communalities for all items were above

0.3. Estimated correlations between factors were primarily

of small to medium size, with the only large correlations

present existing between anxiety, depression and emotional

exhaustion and between satisfaction and control (r \ 0.73).

Potential alternative item-factor configurations motivated

by these stronger correlations, such as the six depression

and anxiety items loading onto a single factor, did not

produce a better fitting model. When this model was

applied to the full dataset the fit was stronger still

(CFI = 0.931, RMSEA = 0.049 and SRMR = 0.046), and

a clear improvement over that of the originally postulated

measurement model.

We concluded the scale validation and construction

analyses by calculating the internal consistency reliability

coefficients for each potential scale. All nine sets of items

showed high internal consistency, with Cronbach’s alpha

[0.75 in each case. We then calculated scale scores for

each set of items for use as our measure of the respective

construct in our subsequent analyses, taking the unweigh-

ted average across all items within the scale. Reliability

statistics are given in Table 1.

Stage 2

This stage entailed exploring the sample characteristics and

the bivariate relationships between our hypothesised ante-

cedents and outcomes. Of the 1,870 cases in our analysis

sample, the average age was 41, and 63% of respondents

were female. Sixty-six percent of cases were married, a

third of the remainder were widowed or divorced, and the

remainder single; just under half (49%) had dependants.

The ethnic makeup of the sample was largely white (75%),

with black (16%) and Asian (8%) workers comprising

almost all of the remainder.

The predominant occupation was nursing: 51% of the

sample was mental health nurses, 26% worked as nursing

assistants or support workers, and a further 5% described

themselves as both mental health nurses and a further

occupation. The remainder of the cases within our analysis

sample were occupational therapist (4%), psychiatrist

(7%), clinical psychologist (2%), and social worker (4%).

The vast majority (94%) of the sample were on permanent

contracts. The mean total hours worked per week was 40

(SD = 11 h); 67% worked shifts, but only 7% reported

permanent working of night shifts. Respondents had spent

an average of 4 years’ service in their respective service

units (median = 3 years) and 8 years in the mental health

service (median = 9 years).

The bivariate correlations between the antecedent and

outcome measures for the analysis sample are given in

Table 1, alongside means and standard deviations.

Stage 3

This final stage of the analyses tested our hypotheses for

each outcome via a series of multilevel regression models.

The initial models simply partitioned the variability in the

respective outcome into that within service units, between

service units and between Trusts, providing a baseline

against which to compare subsequent models containing

predictor variables, and enabling the calculation of the

ICC(1) (Intraclass Correlation Coefficient). For four of the

five outcomes, the proportion of variation due to differ-

ences between Trusts was found to be trivial, falling

below 0.005 in each case (i.e. \0.5% of the total varia-

tion). For intrinsic satisfaction it corresponded to a small

effect (=0.015). Given these findings, and the small

sample size at the Trust level (N = 17), we used a two-

level model of employees nested within service units,

with between-service unit differences hence absorbing

the small amount of between-Trust variability. All five

outcomes exhibited a non-trivial between-service unit

variance component ranging from 5 to 8% of the total

variability (i.e. 0.05 \ ICC(1) \ 0.08), justifying the

exploration of both service unit and employee levels of

variability via a multilevel modelling strategy. The esti-

mates of within- and between-service unit variance for

each outcome are given in Table 2; to enable model

comparison, these values were retrospectively calculated

for the subsample of cases which responded to all vari-

ables used in subsequent models.


123

We then examined the relative contribution of the

demographic and background variables to explaining

within- and between-service unit variability in each out-

come. For continuous variables age and tenure, squared

effects were also considered. Of the control variables used,

service unit type, occupational group and ethnic back-

ground were found to be statistically significant predictors

of one or more of the outcomes.

Specifically, service unit type had a statistically signif-

icant impact on all outcomes. Employees within crisis

teams were typically the most satisfied and felt the most

personal accomplishment, with those within acute and

PICU wards least satisfied, and those within acute, older

patient and forensic wards most likely to feel lower levels

of accomplishment. For anxiety, depression and emotional

exhaustion, respondents from within acute wards and

CMHT were most likely to report high levels, and those

working within rehabilitation the least. Ethnic group was

related to intrinsic satisfaction, personal accomplishment

and emotional exhaustion; for the first two of these out-

comes, black and Asian workers were most likely to report

higher levels; conversely, white workers were most likely

to be emotionally exhausted, with Asians least likely. The

effect of occupational group also varied by outcome:

therapists and psychologists were more likely to report

high levels of intrinsic satisfaction with their jobs and

lower levels of depression, particularly compared to mental

health nurses and social workers. However, mental health

nurses and social workers had higher levels of emotional

exhaustion and anxiety, with nursing assistants and support

workers having the lowest predicted levels when other

control variables were held constant. However, nursing

assistants and support workers, along with social workers,

were also the most likely to report low levels of personal

accomplishment; the highest levels were associated with

the distinct group of mental health nurses who also fulfilled

other occupational group roles.

These three variables, service unit type, occupational

group and ethnic background, were retained for the next

stage of the modelling process; their impact on each out-

come in terms of individual effects is summarised in

Table 2, and in terms of model improvement in Table 1,

Table 1 Internal consistency reliability of, summary statistics for, and correlations between outcome and antecedent measures

Scale Number

of items

in scale

Reliability

(Cronbach’s

alpha)

Mean

scale

score

(N)

Mean

scale

score

(mean)

Mean

scale

score

(SD)

Pearson’s correlation coefficient

1 2 3 4 5 6 7 8

1. Intrinsic job

satisfaction (high

score = high

satisfaction)a

5 0.88 1,836 3.39 0.80

2. Anxiety (high

score = high anxiety)a3 0.77 1,807 2.53 0.75 -0.40

3. Depression (high

score = high

depression)a

3 0.83 1,807 2.08 0.84 -0.53 0.64

4. Emotional exhaustion

(high score = high

exhaustion)b

6 0.91 1,828 2.51 1.50 -0.48 0.57 0.66

5. Personal

accomplishment (high

score = high

accomplishment)b

6 0.79 1,804 4.31 1.11 0.33 -0.15 -0.28 -0.14

6. Job control (high

score = high control)a6 0.89 1,837 3.13 0.87 0.56 -0.21 -0.31 -0.25 0.26

7. Work demands (high

score = high work

demands)a

7 0.92 1,839 2.81 1.01 -0.33 0.43 0.38 0.58 -0.11 -0.13

8. Colleague support (high

score = high support)a4 0.95 1,835 3.60 0.92 0.32 -0.19 -0.26 -0.22 0.23 0.24 -0.16

9. Ward or team manager

support (high

score = high support)a

3 0.92 1,819 3.40 1.16 0.41 -0.22 -0.29 -0.29 0.19 0.27 -0.24 0.39

a Scale range: 1–5b Scale range: 0–6


123

again using the subsample that also responded to the sub-

sequently examined measures of demands, control and

support. Across the five outcomes, they accounted for a

small amount of variability (between 1 and 3%) within

service units, i.e. intra-individual differences, and for a

more substantial 15–47% of the variability between service

units. The other control variables considered did not have

any statistically significant unique effects upon the well-

being outcomes.

Variables measuring demands, control and support

were then entered to test the model of additive effects; each

was first standardised to avoid collinearity issues in the

subsequent testing of the multiplicative model. The main

effects each of job control, ward or team manager support,

and colleague support had negative linear effects upon each

of anxiety, depression, and emotional exhaustion. Simi-

larly, significant positive effects of work demands were

also found for each of these outcomes. Likewise, both

support variables and control had a positive impact upon

intrinsic satisfaction, with demands having a negative

impact. Only for personal accomplishment were the effects

of our antecedents less powerful, with only colleague

support and job control attaining significant positive effects

at the p \ 0.005 level. The estimated coefficients for fixed

and random effects for each outcome at this stage of the

analysis are given in Tables 3 and 5 respectively. Note that

demands, support and control together reduced both the

initial unexplained within-service unit and between-service

unit variance for each outcome by a further substantial

amount on top of that already explained by the control

variables (between 9 and 42%, and between 29 and 48%).

The model deviance statistics, given by the -2log likeli-

hood statistic, were also dramatically reduced, though due

to the estimation method applied (residual maximum

likelihood) and the non-nested nature of these models with

respect to those (control variables only) emerging from the

first stage of the model building process, formal tests of the

reduction in deviance (i.e. the improvement in the fit of the

model as a whole) were not possible.

Allowing slope variation for each predictor in turn (i.e.

the effect of each predictor to vary by service unit) did not

produce statistically significant slope variance coefficients,

nor significantly improve the fit of the model for any out-

come, indicating that the effects of demands, control and

support described above were applicable across the service

units within the sample.

Table 2 Effects of statistically significant control variables upon each outcome

Predictors B Coefficients for each predictor’s fixed effect upon each outcome

INTSAT

(N = 1,694)

ANX

(N = 1,664)

DEP

(N = 1,664)

EE

(N = 1,688)

PA

(N = 1,672)

Occupational group dummies (vs. ref cat: mental health nurses)

Social worker -0.137 0.077 0.034 -0.039 -0.201

Nursing assistant/support worker -0.015 -0.163* -0.081 -0.490* -0.086

Occupational therapist 0.253 -0.183 -0.213 -0.138 0.092

Psychiatrist 0.183 0.023 -0.162 -0.051 0.097

Clinical psychologist 0.278 -0.058 -0.368 -0.359 0.120

Mental health nurse plus other occupation 0.146 -0.079 -0.154 -0.214 0.254

Occupational group: total fixed effect F = 3.575* F = 3.145* F = 2.653 F = 5.564* F = 1.961

Service unit type dummies (vs. ref cat: crisis teams)

Acute -0.272* 0.169 0.276* 0.439* -0.327*

PICU -0.241 0.117 0.226 0.252 -0.224

CAMHS -0.080 -0.054 0.060 0.100 -0.018

Forensic -0.205 -0.082 0.082 0.149 -0.601*

Rehabilitation -0.026 -0.141 -0.037 -0.345 -0.258

Older adult -0.172 -0.111 0.111 0.223 -0.395

CMHT -0.153 0.226 0.323* 0.624* -0.053

Service unit type: total fixed effect F = 2.190 F = 4.988* F = 3.402* F = 4.546* F = 4.788*

Ethnic group dummies (vs. ref cat: White)

Black 0.257* 0.049 0.108 -0.076 0.296*

Asian 0.255* -0.084 -0.096 -0.487* 0.167

Mixed/other 0.135 -0.047 -0.020 -0.553 -0.091

Ethnic group: total fixed effect F = 9.068* F = 0.932 F = 1.789 F = 5.751* F = 5.535*

* p \ 0.005


123

We then added squared effects of each of these pre-

dictors to test for any curvilinear relationships with each

outcome. The only statistically significant effects at the

p \ 0.005 level were for colleague support on intrinsic

satisfaction and depression, and of job control on personal

accomplishment. In the first two instances the curvilinear

relationship reflected a ceiling or basement effect. In other

words, the benefit of support diminished in strength as the

amount of support increased and was not apparent for very

high levels of support, suggesting that there comes a sat-

uration point at which giving further support is no longer

worthwhile. Conversely, the benefits of control on personal

accomplishment only became apparent once control

reached moderate levels, suggesting that a very small

amount of control is no better than none at all.

Finally, we examined the multiplicative effects between

demands, support and control. These consisted of the five-

two-way interactions and the two- to three-way interactions

between either support variable, and one or both of

demands and control. Following the advice of Edwards [8],

we retained the squared effects of each variable within the

model to ensure that any interactions effects detected were

not caused by underlying polynomial effects of a single

variable.

In contrast to the additive model, the support for the

multiplicative Karasek model, according to which the

interactions amongst demands, control and support impact

upon well-being, is less strong. Table 4 reports the models

that include the key interaction terms; for these analyses we

have also indicated results at the p \ 0.05 level due to the

increased measurement error and reduced power inherent

in testing such multiplicative effects.

Across the different measures of well-being the pattern

of statistically significant interaction effects varied. How-

ever, for four of the outcomes the combined effects of the

multiplicative predictors reduced the unexplained within-

service unit variability by a small amount (1%) on top of

that already explained by the control variables and main

effects; further details for each outcome are given in

Table 5.

The only support from the tests for two-way interactions

at the p \ 0.005 level is for the combination of ward or

team manager support and control on intrinsic satisfaction;

specifically, the importance of having control in terms of

boosting satisfaction diminished when ward or team man-

ager support was high. There is weaker evidence that the

importance of job control in increasing satisfaction was

amplified when work demands were high. This effect was

repeated when the outcome variable was depression, i.e.

the importance of job control on reducing depression was

enhanced when work demands were high. Likewise, the

predicted positive impact of work demands on anxiety

diminished slightly as ward or team manager support

increased. Taking these results together, it appears that

high levels of support and control, respectively can

mitigate the worst effects of demands on anxiety and

depression.

Finally, for both anxiety and depression, similar three-

way interactions between demands, control and colleague

support were found. These both indicated that, as colleague

support decreased, the importance of job control in miti-

gating the impact of demands upon well-being (both anx-

iety and depression) was enhanced, as illustrated in Figs. 1

and 2. This suggests that support and control are, to an

extent, interchangeable buffers against the negative impact

of demands.

In the cases of personal accomplishment and emotional

exhaustion, none of the interaction effects were found to be

statistically significant.

Discussion

This research has shown that the demand, control and

support model of well-being is applicable to mental health

workers. However, only the additive model is supported

Table 3 Additive model main effects of demands, support and control, having controlled for retained control variables

Predictors B Coefficients for support, demands and control fixed effect upon each outcome

INTSAT

(N = 1,694)

ANX

(N = 1,664)

DEP

(N = 1,664)

EE

(N = 1,688)

PA

(N = 1,672)

(Occupational group: total fixed effect) F = 1.048 F = 0.845 F = 1.674 F = 0.787 F = 1.147

(Service unit type: total fixed effect) F = 1.370 F = 3.444* F = 2.075 F = 1.750 F = 3.395*

(Ethnic group: total fixed effect) F = 8.797* F = 1.716 F = 3.362 F = 3.121 F = 5.985*

Work demands (standardised) -0.144* 0.275* 0.258* 0.768* -0.056*

Colleague support (standardised) 0.110* -0.059* -0.115* -0.115* 0.191*

Ward or team manager support (standardised) 0.133* -0.049* -0.084* -0.148* 0.051

Job control (standardised) 0.442* -0.118* -0.192* -0.257* 0.250*

* p \ 0.005


123

across all five measures of well-being. The multiplicative

model is only partially supported.

The diversity of results for the models that include

interaction terms highlights the discreteness of the different

measures. Further theoretical and empirical work (perhaps

of a different nature to this) is required to explain why the

multiplicative model is seemingly most applicable to the

anxiety and depression dimensions of well-being, whilst

not fitting the personal accomplishment or emotional

exhaustion elements of burnout. This study’s strength is the

large working sample of mental health workers, which was

drawn from all occupational groups and not confined to

inpatient services. The extent to which it is representative

of the population of British mental health workers cannot

be tested, but we have no reason to suspect that it is not

representative of staff in inpatient care, nor that the range

of service units and community services are exceptional,

since we selected them to cover rural and urban areas, large

and small Trusts. Our response rate compares favourably

with studies in the mental health area or more generally,

but there are studies that have achieved higher rates. The

main weakness of the study is its cross-sectional nature,

and we cannot be certain that perceptions of demands,

support and even control are independent from levels of

well-being, though the diversity in the interaction results

gives us some optimism that this is not the case.

Conclusions

By showing that a major theory of work psychology is

applicable to mental health workers, this study is interest-

ing in its own right. It illustrates that the demands made on

people, the amount of control or discretion they have in

their jobs, and the support from both their managers and

colleagues are important focal points for understanding and

helping to improve the well-being of mental health work-

ers. Whereas past studies of the antecedents of morale have

Table 4 Multiplicative model: main and curvilinear effects, and interaction effects of demands, support and control, having controlled for

retained control variables

Predictors B Coefficients for support, demands and control fixed effects upon each

outcome

INTSAT

(N = 1,694)

ANX

(N = 1,664)

DEP

(N = 1,664)

EE

(N = 1,688)

PA

(N = 1,672)

(Occupational group: total fixed effect) F = 1.209 F = 0.858 F = 1.476 F = 0.837 F = 1.558

(Service unit type: total fixed effect) F = 1.311 F = 3.498* F = 2.092 F = 1.761 F = 3.803*

(Ethnic group: total fixed effect) F = 8.795* F = 1.597 F = 3.695 F = 3.460 F = 6.262*

Work demands (standardised) -0.144* 0.274* 0.249* 0.752* -0.064

Colleague support (standardised) 0.106* -0.047 -0.096* -0.114* 0.191*

Ward or team manager support (standardised) 0.130* -0.036 -0.075* -0.130* 0.063

Job control (standardised) 0.441* -0.122* -0.185* -0.247* 0.262*

Work demands (standardised) squared -0.029� -0.018 0.012 0.025 0.009

Colleague support (standardised) squared -0.044* 0.027 0.055* 0.034 0.007

Ward or team manager support (standardised) squared 0.003 0.022 0.010 0.022 0.025

Job control (standardised) squared 0.015 -0.021 0.002 0.022 0.100*

Interaction

Work demands (standardised) 9 job control (standardised) 0.044� -0.026 -0.045� -0.060 0.002

Work demands (standardised) 9 colleague support (standardised) -0.011 0.001 0.005 0.036 -0.056

Work demands (standardised) 9 ward or team manager support

(standardised)

-0.016 -0.032� 0.003 -0.016 -0.043

Job control (standardised) 9 colleague support (standardised) 0.036 -0.003 -0.047� -0.063 0.015

Job control (standardised) 9 ward or team manager support

(standardised)

-0.048* 0.011 0.035 0.026 -0.047

Work demands (standardised) 9 job control (standardised) 9

ward or team manager support (standardised)

0.019 -0.017 -0.025 -0.025 0.007

Work demands (standardised) 9 job control (standardised) 9

colleague support (standardised)

0.023 0.036� 0.050� 0.048 -0.012

* p \ 0.005� p \ 0.05 (marked for multiplicative effects only)


123

Table 5 Random effects and model deviance statistics for models at each stage of the model building process

INTSAT

(N = 1,694)

ANX

(N = 1,664)

DEP

(N = 1,664)

EE

(N = 1,688)

PA

(N = 1,672)

Step 0

Baseline (unconditional) variance components model with no predictor variables

Baseline re2 0.602 0.522 0.664 2.134 1.152

Baseline ru2 0.040 0.034 0.042 0.140 0.051

Baseline -2LL (deviance)a 4,032 3,722 4,121 6,153 5,044

Step 1

Retained control variables only

re2 0.590 0.518 0.657 2.071 1.138

% baseline re2 explained at this step 2 1 1 3 1

ru2 0.034 0.018 0.031 0.097 0.031

% baseline ru2 explained at this step 15 47 26 31 39

-2LL 4,023 3,718 4,120 6,097 5,029

Step 2

Retained control variables and main linear effects of demands, support and control

re2 0.335 0.420 0.514 1.367 1.036

% baseline re2 further explained at this step 42 19 22 33 9

ru2 0.015 0.008 0.012 0.031 0.014

% baseline ru2 further explained at this step 48 29 45 47 33

-2LL 3,086 3,375 3,710 5,393 4,874

Step 3

Retained control variables, main linear, curvilinear and multiplicative effects of demands, support and control

re2 0.329 0.416 0.508 1.364 1.027

% baseline re2 further explained at this step 1 1 1 0 1

ru2 0.015 0.008 0.012 0.031 0.012

% baseline ru2 further explained at this step 0 0 0 0 0

-2LL 3,117 3,418 3,747 5,436 4,905

re2 = unexplained variance within wards, ru

2 = unexplained variance between wards -2LL, F model deviance on F degrees of freedoma Models fitted using residual maximum likelihood, hence precluding direct comparison/testing of change in model deviance between competing

non-nested models (i.e. those containing different predictors)

Low Colleague Support

Work Demands (Standardised)

3.002.001.000.00-1.00-2.00

Pre

dic

ted

An

xiet

y (a

dju

sted

for

cova

riat

es)

4.00

3.00

2.00

1.00

High Colleague Support


3.002.001.000.00-1.00-2.00

Pre

dic

ted

An

xiet

y (a

dju

sted

for

cova

riat

es)

4.00

3.00

2.00

1.00

High Control

Low Control Low Control

High Control

Fig. 1 The multiplicative effect of demands, control and colleague support upon anxiety


123

often investigated a mixture of general and mental health

service-specific indicators without being rooted in an over-

arching theoretical framework, the results of our study sug-

gest that future research should begin from this very well-

established model of occupational stress. This study thus

provides a benchmark model for testing organisational fac-

tors including personnel management methods, top man-

agement leadership styles, and mechanisms for extending

employee involvement beyond the role level. This also

enables future assessment of whether mental health-specific

factors, such as the violence of patients, have their own

discrete influence, or rather are mediated by general demand,

control or support factors. Alternatively, this model could be

developed to assess whether there are aspects of community

work that differentiate it from inpatient service, over and

above any differences in typical demands, control or support

levels. Our survey includes questions on such factors and

future work will be directed at such issues.

Within the NHS, strategies for alleviating mental health

staff stress have traditionally focused on managerial or peer

support, for example via supervision and appraisal, peer

support groups or training. Our findings suggest that such

strategies have the potential to improve morale if they are

experienced as supportive. Colleague support is important

as well as that from managers; thus interventions to

enhance team cohesion and relationships may have

potential, as well as more formal manager support ones.

Current initiatives in inpatient settings (e.g. protected

engagement time) are highly focused on staff–patient

interaction, improving the quality of this and getting staff

out of clinical offices into wards. However, opportunities

for staff to spend more time together may also be necessary

to improve staff morale and thus performance.

That demand is important for well-being seems intui-

tively likely and is unsurprising given a range of studies

that have cited various mental health service-specific

demands as important to staff stress. How demands may be

reduced is rather less obvious, given high demands for

mental health services in general. A beginning might be the

identification of specific tasks or roles where staff are under

high levels of pressure with a view to redesigning the way

teams work to try and alleviate stress experienced in these.

Involvement of staff in these processes should increase

their success. Initiatives to reduce the demands on services

as a whole are difficult to design, but might include clearer

intake criteria and protocols that allow staff to focus on the

activities that are their core roles, rather than expending

effort on, for example, working with patients on matters

that could be better dealt with elsewhere.

The strong association between autonomy and well-

being suggests that this relatively neglected factor should

be given more prominence in policy. We are not aware of

strategies for improving staff morale that have specifically

focused on autonomy. However, interventions for

improving job design have been developed in other set-

tings. A first step would be to investigate in more detail the

organisation of jobs and teams to identify areas in which

autonomy might be increased, especially in groups that

report low levels. Job redesign or changes in management

practices to allow greater autonomy in deciding how to

work would then be feasible, though training initiatives

may well be needed to support these.

Acknowledgments This project was funded by the National

Institute for Health Research Service Delivery and Organisation

Programme (project number/08/1604/142). The views and opinions

expressed in this paper are those of the authors and do not neces-

sarily reflect those of the Department of Health. We wish to

acknowledge the contribution of the other members of the Inpatient

Staff Morale Study research team, and are also very grateful for

extensive support received from the North and South London,

South-West, East of England and Heart of England hubs of the

Mental Health Research Network, and for the helpfulness of staff in

the 136 participating services.

Low Colleague Support


3.002.001.000.00-1.00-2.00Pre

dic

ted

Dep

ress

ion

(ad

just

ed fo

r co

vari

ates

)

4.00

3.00

2.00

1.00

High Colleague Support


3.002.001.000.00-1.00-2.00Pre

dic

ted

Dep

ress

ion

(ad

just

ed fo

r co

vari

ates

)

4.00

3.00

2.00

1.00

High Control

Low Control

Low Control

High Control

Fig. 2 The multiplicative effect of demands, control and colleague support upon depression


123

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Demands, control, supportive relationships and well-being amongst British mental health workers

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