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Ward Climate within a High Secure ForensicPsychiatric Hospital: Perceptions
of Patients and Nursing staff and the Role of Patient Characteristics.
Meike Godelieve de Vries, Inti Angelo Brazil, Matthew Tonkin, Berend
Hendrik Bulten
PII: S0883-9417(15)00273-3
DOI: doi: 10.1016/j.apnu.2015.12.007
Reference: YAPNU 50801
To appear in: Archives of Psychiatric Nursing
Please cite this article as: de Vries, M.G., Brazil, I.A., Tonkin, M. & Bulten, B.H., Ward
Climate within a High Secure Forensic Psychiatric Hospital: Perceptions of Patients
and Nursing staff and the Role of Patient Characteristics., Archives of Psychiatric Nursing
(2015), doi: 10.1016/j.apnu.2015.12.007
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Ward Climate within a High Secure Forensic Psychiatric Hospital: Perceptions of
Patients and Nursing staff and the Role of Patient Characteristics.
Meike Godelieve de Vriesₐ, Inti Angelo Brazilₐ,
b
, Matthew Tonkin
c
, Berend Hendrik Bultenₐ,
d,e
ₐ Forensic Psychiatric Hospital Pompestichting, Division Diagnostics Research and
Education, 6503 CK Nijmegen, The Netherlands
b
Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EZ
Nijmegen, The Netherlands
c
Birmingham City University, The Curzon Building, 4 Cardigan Street, Birmingham, B4
7BD, United Kingdom
d
Behavioural Science Institute (BSI) of Radboud University, 6500 HE Nijmegen, The
Netherlands
e
Academic Centre for Social Sciences (ACSW), Radboud University, 6500 HE Nijmegen, The
Netherlands
Corresponding author:
Meike G. de Vries, MSc,
Pompekliniek, P.O. Box 31435, 6503 CK Nijmegen.
Telephone number: +31243527600
m.de.vries01@pompestichting.nl
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Abstract
Within this study the relationship between patient characteristics (age, length of stay, risk,
psychopathy) and individual perceived ward climate (n=83), and differences between staff’s
and patient perceptions of climate (n=185) was investigated within a high secure forensic
hospital. Results show that therapeutic hold was rated higher among staff compared to
patients, while patients held a more favorable view on patient cohesion and experienced
safety. Furthermore, patient characteristics (age, risk and psychopathy) were found to be
related with individual ratings of ward climate. The findings underline the importance of
assessing ward climate among both patients and staff in clinical practice.
Keywords: ward climate; EssenCES; patient characteristics; forensic setting; nursing staff
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1. Introduction
Ward climate is an important factor within the treatment of inpatients in secure settings and
has been studied for almost 50 years. Ward climate can be seen as a multifactorial construct
including the material, social, and emotional conditions of a given ward and the interaction
between these factors (Moos, 1989; Tonkin, 2015). Ward climate is found to play a role in
therapeutic outcomes like drop out-, release-, and re-admission rates (Moos, Shelton, & Petty,
1973), patient satisfaction (Bressington, Stewart, Beer, & MacInnes, 2011; Middelboe,
Schjùdt, Byrsting, & Gjerris, 2001; Nesset, Rossberg, & Almvik, 2009; Rossberg & Friis,
2004), motivation for treatment (van der Helm, Beunk, Stams, & van der Laan, 2014),
treatment engagement and therapeutic alliance (Long, Anagnostakis, Fox, Silaule, Somers,
West, & Webster, 2011). Climate can be seen as an aspect of program responsivity that
enhances treatment effects (Beech & Hamilton-Giachritsis, 2005; Howells & Day, 2003;
Ward, Day, Howells, & Birgden, 2004). Ward climate has also found to be a determinant of
staff wellbeing, playing a role in staff performance and morale (Moos & Schaefer, 1987), job
satisfaction (Bressington et al., 2011; Middelboe et al., 2001; Rossberg & Friis, 2004), and
occupational stress (Kirby & Pollock, 1995).
The relationship between ward climate and organizational- and therapeutic outcomes
underlines the importance of establishing and maintaining an environment in which
therapeutic progress is encouraged and that supports staff’ ability to deliver responsible high
quality care to their patients. However, creating an optimal climate within a high security
forensic setting can be very challenging due to the complex patient population, involuntary
admission within a closed setting and the balance between security needs and treatment goals
(Burrows, 1991; Campling, Davies, & Farquharson, 2004; Howells, Krishnan, & Daffern,
2007). Moreover, patients and staff members working within forensic psychiatric settings
seem to evaluate ward climate differently (Caplan, 1993; Day, Casey, Vess, & Huisy, 2011;
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Dickens, Suesse, Snyman, & Picchioni, 2014; Howells et al., 2009; Livingston et al., 2012;
Long et al., 2011; Moos, 1975; Rossberg & Friis, 2004; Schalast, Redies, Collins, Stacey, &
Howell, 2008; Morrison, Burnard, & Phillips, 1997). For instance, Howells et al. (2009)
found that patients in a high secure hospital service in the United Kingdom (UK) evaluated
cohesion among patients more favorably than staff members. Another study found that
patients in open, low and medium secure wards of a psychiatric hospital in the UK evaluated
the ward climate as safer than staff members (Dickens et al., 2014). In both studies, staff
members evaluated the therapeutic hold (how much the environment is supportive of therapy
and therapeutic change) more favorably compared to patients. Caplan (1993) found that staff
and patient perceptions differed with regard to several scales of the Ward Atmosphere Scale
(WAS; Moos, 1968; 1989, Moos & Houts, 1974), including order and organization, program
clarity and staff control. Possible explanations given in previous research for the divergent
perceptions between nursing staff and patients are, the different roles and functions that staff
and patients have within a forensic institution (Caplan,1993; Goffman, 1961; Rossberg &
Friis, 2004), and the restrictions to the liberty and personal freedom of incarcerated patients
(Langdon, Cosgrave & Tranah, 2004). Patients’ restricted liberty could also be a potential
explanation for the finding that the perception of climate differs as a function of the level of
security (Dickens et al., 2014; Long et al., 2011; Milsom et al., 2014).
It follows that gaining insight into patients’ and staff’s perception of ward climate is
highly informative and promotes the discovery of potentially meaningful discrepancies
between the groups. Friis (1986) has argued that the patient’s perception of the ward milieu
can be seen as a most important indicator of how the milieu affects the patient. When striving
to keep patients in a responsive therapeutic environment which is designed to address their
needs (in order to enhance treatment efficacy), it is important to have insight in how the
climate is actually perceived by patients. Forensic nurses could use this information in their
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daily work, actively discussing the different views on ward climate within their team and with
their patient group. Together they could identify different needs, create opportunities for
improvement of the treatment milieu and subsequently improve treatment success.
Importantly, however, ward climate perception is also dependent on other factors.
Recent research by Dickens et al. (2014) revealed associations between patient characteristics
and mean evaluation scores of ward climate. They found that female gender positively
predicted patient cohesion and perceived safety measured with the Essen Climate Evaluation
Scale (EssenCES; Schalast et al., 2008) among patients residing in open, low and medium
secure forensic settings. Furthermore, higher perceived risk measured with the Historical,
Clinical and Risk Management 20 (HCR-20; Webster, Douglas, Eaves, & Hart, 1997) was
associated with lower perceived patient cohesion, a diagnosis of personality disorder or
psychosis according to the ICD-10 (WHO, 2010) was related to higher experienced safety,
and higher levels of engagement (i.e., the number of programmed therapeutic sessions
attended over a two-week period) was associated with greater therapeutic hold.
While not accounting for all relationships presented above, the relationship between
ward climate and various environmental, social and individual characteristics might reflect the
interplay between patients’ (security) needs and climate. Hence, individuals at high risk of
showing violence or who are suffering from severe psychiatric problems might have higher
security needs, leading them to be more exposed to physical, procedural and relational
security, ultimately influencing their (perception of) ward climate. Norton (2004) describes
how five functional properties of a ward (containment, support, structure, involvement and
validation) can also reflect the patient’s changing needs, and how the emphasis on these
factors can change during a treatment process (and during crisis situations).
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In contrast to Dickens et al. (2014) there is also research showing that patient
characteristics have a small or no impact on ward climate (Moos, 1997; Pedersen & Karterud,
2007). Pedersen and Karterud (2007) found no substantial associations between patient
characteristics (gender, age, level of education, self reported symptom distress, interpersonal
problems, diagnosis) and individual ratings of treatment milieu. Data were collected from
patients (71% women) suffering mainly from personality, mood and anxiety disorders who
had been admitted to day-treatment units. Pedersen and Karterud (2007) argue that since
differences between patients’ views on ward climate cannot be attributed to patient
characteristics they must be largely idiosyncratic. Alternative explanations for the discrepant
findings with regard to the role of patient characteristics might be found in differences in
methodology (using the EssenCES versus the WAS for assessing climate), and different
clinical setting/samples used in the studies of Dickens and colleagues (2014) and Pedersen
and Karterud (2007).
Contradictory findings highlight the importance of conducting more research in order
to disentangle the possible relationships between patient characteristics and ward climate
within secure forensic settings. Gaining more knowledge about these relationships could be
beneficial for clinical practice by providing guidance for active management of ward climate.
Hence, when striving to keep patients in a therapeutic environment designed to address their
needs, taking into account individual patient characteristics is essential. In order to do so,
more research is needed, demonstrating whether or not certain personal characteristics are
related to the perception of ward climate. When relationships and underlying mechanisms are
clearer, this knowledge could be used to guide assessment, evaluation, assignment to specific
wards, composing patient groups and staff training.
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Since there are very few studies of the relationship between ward climate and patient
characteristics this study contributes to an under-explored but important area. The aim of this
current study is to provide more insight into the relationship between patient characteristics
and perceived ward climate. Based on previous findings, the demographic characteristics that
might be related to perception of ward climate targeted in the present study were patients’ age
(Campbell, Allan, & Sims, 2014; Middelboe et al., 2001; Pedersen & Karterud, 2007), length
of stay within the facility (van der Helm et al., 2014), and risk of violence (Dickens et al.,
2014). With respect to pathological personality features, there are reports that psychopathy
may be a key determinant of climate in forensic therapeutic settings (Harkins, Beech, &
Thornton, 2012). Psychopathy is a severe condition characterized by a combination of
personality characteristics entailing disturbed interpersonal-affective functioning combined
with high anti-sociality (Neumann, Hare, & Newman, 2007). Therefore, the impact of having
psychopathic features on the perception of ward climate was also assessed. This study has an
explorative nature, since the literature provides inconclusive findings and therefore precludes
the formulation of clear hypotheses.
As very little research on ward climate has been conducted outside of the US and the
UK, this study also aims to assess whether the differences between patients’ and staff’s
perceptions of ward climate can be found in the high secure forensic setting in the
Netherlands. Based on previous findings, we hypothesized that patients should report higher
levels of experienced safety and patient cohesion compared to staff members and that staff
members should report higher levels of therapeutic hold compared to patients.
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To conclude, the aim of this current study is to provide more insight into the
differences between patients’ and staff’s perceptions of ward climate, and into the relationship
between patient characteristics and perceived ward climate.
2. Material and Methods
2.1 Subjects
Data were collected within a high secure forensic psychiatric institution in the
Netherlands. In the Netherlands, offenders who have committed a serious crime, (partly) due
to a psychopathological condition (Diagnostic and Statistical Manual of Mental Disorders,
version IV-TR axis-I and/or axis–II disorder; American Psychiatric Association, 2000), can be
assigned to a measure to be treated on behalf of the state (Ter Beschikking Stelling; TBS).
TBS is not a punishment, but an entrustment act for offenders with mental disorders, which
aims to protect society against the risk of recidivism through incarceration and treatment.
Between 2007 and 2012 a total of 1399 measurements of the EssenCES were obtained
(891 EssenCES scored by staff members and 508 by patients, including repeated measures).
In order to include as many participants as possible within the analysis of this present study
two sub-samples were extracted from this total dataset. One sample was used to compare staff
members and patients’ views on ward climate. Therefore, only wards where at least half of the
staff members and half of the patients participated during the same measurement point, were
selected. A response rate of at least 50% seemed sufficient to obtain a climate profile
(Dickens et al., 2014). Schalast et al. (2008) argue that it is not necessary for all patients and
staff to fill in the questionnaire to get a realistic or valid view. This method resulted in a
sample of 72 patients and 113 staff members from 13 wards. In order to investigate the
reliability of the scale within this Dutch forensic sample and in order to look at the role that
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individual patient characteristics play in the perception of ward climate, the first measurement
from each participant (excluding repeated measures), was extracted from the total dataset to
form a second sample. This resulted in a sample of 373 participants, 154 patients and 219
staff members. Demographic and clinical characteristics of the participating patients are
shown in table 1. All participating patients were male and were diagnosed with one or more
diagnoses on Axis I and/or Axis II defined according to the DSM IV/V (American Psychiatric
Association, 2000).
2.2 Procedure
The data collection was part of routine evaluation of the ward climate within the
institution. Staff members working on the wards and patients that resided on the wards were
routinely (three times per year) asked to complete the EssenCES. The researcher gave oral
and written information concerning the data collection, the study aims and objectives. Staff
members completed the measures during work hours, patients were rewarded with € 2,35. The
completed questionnaires were returned to the researcher, after which the scores were entered
into SPSS version 20 (IBM, SPSS Statistics) for analyses.
Data on patient characteristics (age, length of stay, disorder, risk, and psychopathy)
were extracted from the clinical records of the patients and added to the SPSS database.
Collection of data about, e.g., mental disorders (DSM IV/V) and level of risk (HCR-20) is
important and mandatory upon admission to the forensic mental health system. As the HCR-
20 (and in some cases the PCL-R) are administered multiple times in order to monitor a
patient’s risk, the assessment that had taken place most closely to the assessment of climate
was used in this study.
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2.3 Measures
2.3.1 Climate
The Essen Climate Evaluation Schema (EssenCES; Schalast et al., 2008) is a 17-item
questionnaire measuring three aspects of climate in forensic services (Therapeutic Hold (TH),
Experienced Safety (ES), and Patients Cohesion and Mutual Support (PC)). The first and final
items of the questionnaire are not scored, since they are meant to start and end the
questionnaire with a positive note. Examples of items representing the different factors are
‘The patients care for each other’ (PC), ‘Really threatening situations can occur here’ (ES),
‘On this ward, patients can openly talk to staff about all their problems’ (TH). The Dutch
translation of the EssenCES was used (Bulten & Fluttert, 2007). Ratings were obtained using
a Visual Analogue response Scale (VAS) ranging from ‘not at all’ (0) up to ‘very much’
(100). The scores on items 3, 6, 8, 9, 12, 13, 15 were reversed prior to analyses, as a result
high scores reflect a positive perceived ward climate. Several studies provide good empirical
support for the psychometric properties of the EssenCES (Schalast et al., 2008; Howells et al.,
2009; Tonkin et. al., 2012). Tonkin (2015) reported in a review ten studies on the internal
consistency of the EssenCES, the mean Cronbach’s alpha’s ranged from: .82 (PC), .77 (ES),
and .81 (TH).
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2.3.2 Risk for violence.
The HCR-20 (Webster et al., 1997) is a risk assessment tool broadly used by clinicians to
assess risk of future violence. The HCR-20 reliably predicts future violence (Douglas &
Webster, 1999). The HCR-20 consists of 20 items (rated on a three-point scale 0 = criteria is
not present, 1 = possibly present and 2 = definitely present) divided into three subscales,
Historical, Clinical, and Risk management that relate to risk factors in the past, present and
future.
2.3.3 Psychopathy
The Hare Psychopathy Checklist – Revised (PCL-R; Hare, 2003) is a clinical tool for
assessing psychopathy. The 20 items are scored (as 0, 1 or 2) by two independent trained
raters based on a semi-structured interview and case-history information. Sum scores can be
obtained for four facets, reflecting interpersonal problems, affective problems, impulsive
behavior lifestyle and antisocial behavior (Hare, 2003; Hare & Neumann, 2005).
2.4 Statistical Analyses
Mean scores and standard deviations on the EssenCES subscales were calculated.
Internal consistency was examined using Cronbachs alpha (α) and Corrected Item Total
Correlation (CITC) coefficients. Internal consistency was examined for the sample as a
whole, as well as for staff and patients separately.
A one-way multivariate analysis of variance (MANOVA) was conducted to see
whether scores on the subscales of the EssenCES differed between respondent types (staff,
patient). The subscores on PC and TH of two staff members and the sub score of one staff
member on ES could not be taken into account due to missing items on the EssenCES.
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To assess the relationship between patient characteristics and ward climate path
analyses were conducted using Mplus v7.0 (Muthen & Muthen, 1998). Age, length of stay,
the four facet scores of the PCL-R and the scores for the three scales of the HCR-20 were
entered as predictors, while the scores on the three scales of the EssenCES served as mutually
related dependent variables. Not all patients had a score on the PCL-R (PCL-R Interpersonal
scale: 37 missing values; Affective scale: 46 missing values; Lifestyle scale: 48 missing
values; Antisocial scale: 55 missing values). Also not all the HCR-20 scores were available
(HCR-20 Historical scale 33 missing values; Clinical scale: 23 missing values; Risk scale: 24
missing values).
Eighty three patients had scores on all variables and were included in the analyses. A
Bayesian estimator was used with 5 Markov chain Monte Carlo (MCMC) chains and 75000
iterations using the default Gibbs sampler (PX1) in Mplus. The Bayesian estimator has been
found to outperform traditional maximum likelihood estimators (Muthn, 2010) and provides
reliable results even in relatively small samples (e.g., n=50) (Scheines, Hoijtink, & Boomsma,
1999). As is common in Bayesian analyses, the first half of the MCMC iterations was
discarded to reduce the effect of the initial values found by the chains (burn-in trials). Model
fit was determined using three different fit indexes for Bayesian testing: i) Chi-Square tests to
conduct Posterior Predictive Checking (95% credibility interval; CI), ii) the Posterior
Predictive P-value (PPP-value) and iii) convergence according to the Gelman-Rubin criterion
based on the potential scale reduction (PSR) factor for each parameter (Gelman & Rubin,
1992; Gelman et al., 2004, pp. 296- 297). In general, the 95% CI for the Chi-square posterior
predictive checking should include the value 0 (in contrast to non-Bayesian frameworks), the
PPP-value should be close to the value 0.50 and convergence is achieved when the PSR is
below 1.05 (Muthen & Muthen, 1998). Significance of the regression weights were
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determined based on the 95% CIs of the Bayesian posterior distribution. Regressor CIs not
containing the value 0 were considered significant.
3. Results
From the patient group (n = 154) demographic and clinical characteristics are displayed in
table 1.
Table 1. Demographic and clinical characteristics of the patient sample
1
.
n
M
SD
Range
Age (in years)
154
39.25
10.02
22 - 67
Length of stay (in months)
154
29.20
24.87
0 - 145
EssenCES* Patient cohesion
154
258.79
110.20
0 - 500
EssenCES Experienced Safety
154
266.10
113.79
0 - 494
EssenCES Therapeutic Hold
154
230.16
124.49
0 - 500
PCL-R Total score**
120
19.65
7.05
6 - 36
PCL-R Interpersonal scale
117
3.19
2.00
0 - 8
PCL-R Affective scale
108
5.24
2.21
0 - 8
PCL-R Lifestyle scale
106
5.15
2.44
0 - 10
PCL-R Antisocial scale
99
5.22
2.80
0 - 10
HCR Total score***
132
3.01
1.41
1 - 5
HCR Historical scale
121
14.01
2.53
6 - 18
HCR Clinical scale
131
4.60
1.95
0 - 9
HCR Risk scale
130
4.81
2.40
0 – 10
* the minimum and maximum scores that can be obtained for the subscales of the EssenCES
using a Visual Analogue Response scale are: 0-500.
** the minimum and maximum scores that can be obtained for the PCL-R are: Total: 0-40;
Interpersonal and Affective scale: 0-8;Lifestyle and Antisocial scale:0-10.
*** the minimum and maximum scores that can be obtained for the HCR-20 are: Total: 0-5;
Historical scale: 0-20; Clinical scale: 0-10; Risk scale:0-10.
3.1 Internal Consistency
1
Mean scores and standard deviations of the sub-sample used in the path analysis are
comparable with the scores reported here. Descriptives are available from the first author on
request.
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The internal consistency of the EssenCES was assessed using Cronbach’s alpha (α) and
Corrected Item Total Correlation (CITC) coefficients. Within the total sample Cronbach’s α
ranged from .73 to .84 (see table 2). CITC ranged from .45 to .74. According to Helmstadter
(1964) a CITC above .50 is considered high and α should exceed .70. Furthermore removal of
an item would be wise in case of a CITC below .20.
Within the patient sample Cronbach’s α ranged from .76 to .84. CITC ranged from .37
to .74. Within the staff sample CITC ranged from .30 to .76 and Cronbach’s α ranged from
.67 to .85. No CITC values below .20 were found and almost all Cronbach alpha values
exceed .70, except for TH in the staff sample (α = .67). These findings indicate satisfactory
internal consistency for the Dutch translation of the EssenCES. Since Cronbach alpha values
are sensitive to the length of a scale, it is common to find lower α values (around .5) for short
scales like the EssenCES (Cortina, 1993).
Table 2. Internal consistency (Cronbach’s α) of the EssenCES.
Scale
Patients (n=154)
Staff (n=219)
Total (n=373)
Patient Cohesion
.82
.85
.84
Experienced Safety
.76
.70
.73
Therapeutic Hold
.84
.67
.84
3.2 Patient perception versus staff perception of climate
A MANOVA was conducted to compare patients and staff in terms of their scores on the
subscales of the EssenCES (see table 3). A statistically significant MANOVA effect was
obtained, Pillais’ Trace = .31, F(3, 178) = 27.16, p < .001. The univariate F tests showed there
was a significant difference between staff and patients on all subscales of the EssenCES, PC:
F = 8.07, df = (1,180), p = .005; ES: F = 21.23, df = (1,180), p < .001; and TH: F = 37.24, df
= (1,180), p < .001.
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Table 3. Results of the MANOVA analysis.
Patients
n = 72
Staff
n = 110
Statistic
M (SD)
M (SD)
F (df)
p
Multivariate test
27.16 (3,178)
< 0.001
Univariate tests
Patient cohesion
250.19 (111.98)
209.69 (80.24)
8.07 (1,180)
.005
Experienced safety
268.15 (101.11)
208.72 (72.81)
21.23 (1,180)
< 0.001
Therapeutic hold
241.51 (127.18)
327.23 (60.34)
37.24 (1,180)
< 0.001
3.3 Patient characteristics predicting perceived ward climate
The model as a result of the Bayesian path analysis is displayed in figure 1. Age, length of
stay, the 4 facet scores of the PCL-R and the scores for the 3 scales of the HCR-20 were
entered as predictors, while the scores on the 3 scales of the EssenCES served as mutually
related dependent variables. The item of the HCR-20 measuring psychopathy was left out
when computing the historical scale since psychopathy was assessed in this study with a fine
grained psychopathy measure, the PCL-R.
Regarding model fit, the 95% CI of the Chi-square check of the posterior predictive
ranged from - 24.01 to 29.38, PPP-value was 0.43 and the PSR was below 1.05. Thus, all
model fit indexes indicated good model fit. Results showed (see table 4) that patient cohesion
was negatively predicted by the antisocial facet of the PCL-R (β=-.32) and positively by the
historical factor of the HCR-20 (β=.30). Experienced safety was positively predicted by the
historical factor of the HCR-20 (β=.33). Therapeutic hold was positively predicted by age (β=
.27), the interpersonal facet of the PCL-R (β=.23), and negatively by the clinical factor of the
HCR-20 (β=.-.34). PC was related to TH r=.35, and ES r=.39.
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Table 4. Standardized results of the Bayesian path analysis.
95% C.I.
Dependent variables
Predictor
Estimate (β)
Lower 2.5% - Upper 2.5 %
Patient Cohesion
Age
-0.005
-0.209 0.203
Length of stay
-0.035
-0.234 0.166
PCL-R Interpersonal
-0.039
-0.274 0.199
PCL-R Affective
0.165
-0.086 0.396
PCL-R Lifestyle
0.051
-0.206 0.308
PCL-R Antisocial
-0.324
-0.611 -0.003 *
HCR-20 Clinical
0.027
0.251 0.302
HCR-20 Historical
0.301
0.001 0.575 *
HCR-20 Risk
0.090
-0.180 0.348
Experienced Safety
Age
0.153
-0.053 0.342
Length of stay
0.099
-0.100 0.288
PCL-R Interpersonal
-0.016
-0.245 0.217
PCL-R Affective
0.137
-0.106 0.368
PCL-R Lifestyle
0.183
-0.074 0.425
PCL-R Antisocial
-0.194
-0.485 0.118
HCR-20 Clinical
-0.027
-0.295 0.245
HCR-20 Historical
0.333
0.037 0.593 *
HCR-20 Risk
-0.114
-0.363 0.151
Therapeutic Hold
Age
0.265
0.070 0.440 *
Length of stay
0.027
-0.163 0.214
PCL-R Interpersonal
0.229
0.001 0.438 *
PCL-R Affective
-0.047
-0.276 0.183
PCL-R Lifestyle
0.155
-0.094 0.389
PCL-R Antisocial
-0.109
-0.396 0.193
HCR-20 Clinical
-0.344
-0.581 -0.076 *
HCR-20 Historical
0.131
-0.151 0.399
HCR-20 Risk
0.037
-0.214 0.285
Patient cohesion
Experienced safety
0.393
0.172 0.577 *
Patient cohesion
Therapeutic hold
0.353
0.126 0.545 *
Experienced safety
Therapeutic hold
0.229
-0.007 0.443
Figure 1. Bayesian path analysis, only significant relationships are displayed, estimate (β).
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4. Discussion
The aim of this present study was to provide insight into the relationship between patient
characteristics and perceived ward climate, and to see whether the differences between
patients’ and staff’s perceptions of ward climate previously found in the US and the UK can
be found in a high secure forensic setting in the Netherlands.
The results show that staff and patients from high secure forensic wards differ in the
way they evaluate climate. Therapeutic hold was rated higher among staff members compared
to patients, which is consistent with previous research (Dickens et al., 2014; Howells et al.,
2009; Long et al., 2011; Schalast et al., 2008). The consistency of this finding across different
facilities, ranging from open to high security, indicates that this is quite a stable difference in
perception between these two groups. With regard to the other two subscales of the EssenCES
(PC and ES), patients held a more favorable view compared to nursing staff. These results are
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in line with previous studies, reporting differences between staff’s and patients’ perceptions
on ES (Dickens et al., 2014) and PC (Howells et al., 2009).
The finding that staff members and patients differ on all three subscales of the
EssenCES supports the notion that the different roles and functions that staff and patients
have within a forensic institution influence their perception of the ward climate. In line with
that argumentation, a potential explanation for the difference on therapeutic hold is perceived
lack of control of patients on the therapeutic environment (Brunt & Rask, 2007; Dickens et
al., 2014). In their study, Brunt and Rask (2007) interpreted patients’ negative statements
about personal qualities of staff as an indication of experiences of repressiveness in a coercive
system. Another possible mechanism explaining the difference found between patients and
staff on ward climate could be an interpretation bias. Research on self-serving bias indicates
that being observer or actor of a task influences the attributions made (Campbell & Sedikides,
1999). Since therapeutic hold targets mostly staff’s work and patient cohesion and safety
could be interpreted as more influenced by actions of the patient group, the differences are
understandable. However, in order to disentangle the specific factors playing a role in the
differences in perceptions between patient and staff further research is needed. In the future, it
would be beneficial to administer a measure of socially desirable responding or a measure of
attribution bias or locus of control alongside the EssenCES, which could then be controlled
for in any subsequent analyses. Furthermore, qualitative research (in-depth interviews with
patients and staff) might help to gain more insight into the underlying processes. However,
these kinds of methods/measures assume that people are capable of introspection, and that
they are motivated and willing to report their attitudes and beliefs accurately. Assuming that
this is not always the case, another interesting direction might be found in more implicit
measures. Future research could focus on implicit associations or automatic responses staff
members might have towards specific patients or their attitude towards several treatment
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orientations. There are some indications that implicit attitudes are related to nursing behavior.
Hence, medical visit communication between nurses and patients and patients’ perceptions of
care seem to be associated with both implicit attitudes about race and stereotyping (Cooper et
al., 2012). Furthermore, implicit prejudice is found to mediate the relationship between
experiences of job stress and intention to change jobs among drug and alcohol nurses (Von
Hippel, Brener, & von Hippel, 2008).
With regard to the relationship between patient characteristics and ward climate, the
results of this current study show that high scores on the patient cohesion subscale are
negatively associated with the antisocial facet of the PCL-R, and positively with the historical
factor of the HCR-20. Contrary to this finding, Dickens et al. (2014) found a negative
predictive value of the HCR-20 total-score on the level of cohesion on a ward. The
explanations given by Dickens and colleagues are that individuals with a higher level of risk
of future violence influence cohesion among patients negatively or that the risk of violence
might be lowered by more cohesion on a ward. It is important to note that the
operationalization of the outcome measures used in the present study and the study of Dickens
and colleagues differs. While Dickens focused on how patient characteristics are represented
on a ward and how this relates to ward climate as a group score (mean of all patients’ scores),
this current study used individual scores on patient characteristics as predictors and individual
perceived ward climate as an outcome measure. Also, there are several other differences
between the study of Dickens and colleagues and our study that could possibly account for the
different findings. For example, 62 % of the data collected by Dickens et al. (2014) came
from low security wards while the data of this study were collected within a high secure
forensic setting. Risk was also operationalized differently, while Dickens and colleagues used
the total score of the HCR-20, this current study uses the three subscales of the HCR-20,
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historical, clinical, and risk management in order to get more detailed insight into the
relationships between risk and climate.
The results show that differentiating between the three risk scales of the HCR-20 is
useful in the prediction of climate. Hence, in predicting individual perceived climate the
historical scale of the HCR-20 is found to positively predict patient cohesion and experienced
safety. A potential underlying mechanism explaining the relationship between the historical
risk factor and patient cohesion might be that the current environment (within the clinic)
might be significantly better compared to patients’ past environment with regard to the
amount of support and safety. It might be that patients scoring high on the historical risk
factors have experienced low levels of support during their life and evaluate even a little
amount of support more positively than patients who are used to living in a supportive
environment. With regard to safety, patients scoring high on the historical risk factors might
be less susceptible to feeling a lack of safety due to their history of violence and/or
personality disorder. It could also be the case that they are the more aggressive/intimidating
patients on a ward, which causes other patients to feel unsafe.
A potential underlying mechanism of the negative predictive value of the antisocial
facet of the PCL-R in patient cohesion might be that these antisocial patients find it difficult
to interact with other patients. With regard to the relationship between psychopathy and social
functioning and adaptation in a normal population, Baird (2002) demonstrated that primary
psychopathy (egocentricity, manipulativeness, deceitfulness, and having a lack of remorse) is
not detrimental but also does not benefit social functioning. Moreover, it was found that
secondary psychopathy (antisocial behaviors and an unstable, self-defeating lifestyle) was
related to a lack of success in social functioning. Furthermore, patients scoring high on the anti-
social facet of the PCL-R might have difficulties adhering to clinic/ward rules, leading them to
be either frequently secluded or socially isolated from the group. In research among juvenile
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psychiatric inpatients, psychopathy has been associated with poorer institutional adjustment in
the form of increased number of intensive supervision placement as a result of fighting,
refusing to attend school or other mandatory activities, hurting oneself or others (Taylor,
Kemper, & Kistner, 2007). A link between psychopathy and removal for serious non-
compliance and rule violation has also been found in incarcerated female offenders in a
substance abuse treatment program (Richards, Casey, & Lucente, 2003).
Therapeutic hold was predicted by three of the nine included patient characteristics.
This result differs from results found in previous research where age was not found to be
related to the perception of ward climate (Campbell et al., 2014; Middelboe et al., 2001;
Pedersen and Karterud, 2007). In our study there was a positive relationship between age and
therapeutic hold. A potential underlying mechanism might be that with increasing age patients
become wiser, and calmer. Patients might get more notion of, and respect for the intentions of
staff members for their recovery. There is research demonstrating that age is an important
factor in the early formation of a therapeutic relationship. For example, Rosen, Miller,
Nakash, Halpern and Alegría, (2012) found that matching out-patients from mental health and
substance abuse services and therapists on age positively affected the intake process.
The interpersonal facet of the PCL-R was positively- and the clinical factor of the
HCR-20 was negatively related to the perception of therapeutic hold. One interpretation is
that individuals scoring high on the interpersonal factor of the PCL-R might have more
positive contact with staff members due to their charm and manipulative behavior. In line
with this, a study in a non-forensic sample found that individuals with relatively high scores
on interpersonal and affective aspects of psychopathy did not show impairments in social
adaptation (Baird, 2002). On the other hand it could also be that their grandiose sense of self
worth influences their perception of the therapeutic holding by staff. Patients scoring high on
the clinical factor of the HCR-20 (having more problems) tend to evaluate the climate as less
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therapeutic. It could be that their negative attitude and lack of insight resonates in their
evaluation of the therapeutic holding on a ward. In order to see whether the relationship
between the interpersonal factor of the PCL-R, the clinical risk factor of the HCR-20 and
therapeutic hold is a result of the environment that differs as a function of patient needs, or
whether the explanation lies more within the perception of the patient, it would be desirable to
incorporate measures giving more insight into patients’ (security) needs and the therapeutic
contact between staff and patients (for instance the frequency, perceived quality, and duration
of the time spent with each other) in the future.
The present study demonstrates that there are patient characteristics associated with
individual ratings of ward climate. However, the precise mechanisms underlying the
relationships between patient characteristics and individual perceived ward climate requires
further examination. As mentioned before, the relationship between patient characteristics and
the perception of ward climate might reflect the interplay between patients’ (security) needs
and the environment / climate. As climate needs to be adaptive and responsive to patients’
needs it would be interesting to conduct longitudinal research to assess the perception of ward
climate at regular intervals during several years, to see whether the perception of climate
changes as a function of changes in (security) needs of patients.
The findings of this study add and underline the importance of assessing ward climate
among both patients and staff in clinical practice. Since ward climate is perceived differently
between these two groups, the perception of the staff cannot be regarded as a valid indicator
of how the climate is perceived by patients. Detailed feedback differentiating between
patients’ scores and staff scores could provide insight into potential discrepancies between
groups. When discrepancies between staff and patients views are clear on a ward,
interventions (for example active discussion between staff and patients or staff training) can
take place aimed at fine-tuning climate on a ward. Given that staff and patients differ in their
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perceptions of the ward climate, suggest that interventions designed to improve the perceived
climate on a ward should target different aspects when delivered to staff compared to when
they are delivered to patients. Service managers could choose or design interventions to
improve perceptions of climate in both staff and patients.
Research has shown that active participation of staff (and patients) is a key factor in
the process of improving perceptions of ward climate (James, Milne, & Firth, 1990; Moos,
1973). Nesset, Rossberg, and Almvik (2009) indicate for instance that a three-week staff
training program concerning important aspects of treatment milieu (with a particular focus on
the relationship between patients and nursing staff and staff members’ behavior and their
attitudes towards the patients), can improve ward climate as perceived by patients within a
forensic psychiatric ward. After the intervention, patients reported an increase in a number of
WAS scales, including involvement, support, practical orientation (how much patients learn
practical skills and are prepared for release from a program), order and organization (the
importance of order and organization in a program), as well as a lower level of anger and
aggressive behavior. Another potential important aspect for management of ward climate,
described by Norton (2004), is that patients know what they can expect from the environment
(nurses) and what is expected from them. Norton argues that the overall therapeutic objectives
of a ward need to be clear. These objectives can for instance be documented for staff and
patients, accompanied with methods used on a ward to achieve them. Although additional
research into the relationships between patient characteristics and individual perceived
climate is needed, this knowledge could potentially be beneficial for active management of
ward climate. Knowledge on the relationship between patient characteristics and the
perception of climate on a ward could for instance assist service managers in the composition
of patient groups. Furthermore, insights could be implemented in staff’s training programs,
informing them what they can expect from patients with regard to their perception of climate
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(for instance, which patients might be susceptible for feelings of unsafety or for perceiving
lower levels of therapeutic hold).
There were various limitations to this study that should be noted. Firstly, the sample
was drawn from a single high secure forensic hospital in the Netherlands, limiting
generalizability of the results. Replication of these results is needed in other high secure
forensic hospitals. A second limitation would be missing data as a result of the participation
of patients on voluntary base. It could be that individuals that did not participate in the
assessment have other views on climate than individuals that did participate. Nevertheless, in
order to gain a valid and realistic assessment, we aimed at intrinsically motivated,
spontaneous participation. Therefore, in line with recommendations of the authors of the
EssenCES, assessment took place by inviting instead of urging staff members and patients to
fill in the questionnaire. Also, not all information from clinical files was available for the
researchers for several reasons. For instance, some participants were administered to and
discharged from the clinic before routine assessment of risk became obligatory, some clinical
files were not (yet) up to date and sometimes missing items on a scale resulted in missing
scores on subscales. The possibility that individuals with missing data (that were thus
excluded from the path analysis) would change the results when included can therefore not be
ruled out. Third, our study only entails a couple of patient characteristics that could be related
to the perception of ward climate. Other characteristics that would be worth adding in future
research are for instance, type of offence, treatment engagement, and amount of leave taking.
Despite the limitations of this study, the findings further our knowledge about an
under explored topic, namely the relationship between patients’ characteristics on individual
perceived climate, using sophisticated statistical techniques. Also, this study extends earlier
research conducted mostly in the US and the UK to the high secure forensic setting in the
Netherlands. This study is the first to demonstrate differences between staff members and
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patients on all three factors of climate measured with the EssenCES within a high secure
forensic setting. Nursing staff and management within the forensic setting could use the
knowledge derived from this study in their challenging task of setting and maintaining a ward
climate supportive of treatment success for the whole group and well as for the individual
patient.
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Acknowledgements
We would like to thank the Pompestichting and all the patients and staff members that
participated in our study. In addition we would like to thank Marjolein Kobes and Rachel
Arends for their work during data collection.
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