Full text of DOI:10.1046/j.1365-2850.2000.00285.x

Journal of Psychiatric and Mental Health Nursing, 2000, 7, 213–220
Detecting nurse-perceived patient treatment difficulty of
psychiatric patients in hospital: an evaluation of a patient
assessment sheet
R. F. CHIOVITTI rn bscn mscn edd & R. GALLOP rn bscn mscn phd
Faculty of Nursing, University of Toronto, 50 St. George Street, Toronto, Ontario, M5S-3H4, Canada
CHIOVITTI R. F. & GALLOP R. (2000) Journal of Psychiatric and Mental Health Nursing
7, 213–220
Detecting nurse-perceived patient treatment difficulty of psychiatric patients in
hospital: an evaluation of a patient assessment sheet
Correspondence:
Rosalina Fiorino Chiovitti
Faculty of Nursing
University of Toronto
50 St. George Street
Toronto
Many changes have occurred in hospital psychiatric care, including decreased length of hos-
pital stay and increased patient acuity. These changes highlight the need for nurses to ade-
quately assess and formally document patient treatment difficulties. The purposes of this
study were to determine the ability of the Patient Assessment Sheet (PAS) to predict patient
‘problems’ that psychiatric nurses perceived as associated with patient treatment difficulty,
and to identify the patient problems missing from the PAS. These purposes were accom-
plished by comparing the PAS to the Hospital Treatment Rating Scales (HTRS). A corre-
lational design and multiple linear regression technique were used. Eight psychiatric
registered nurses assessed a total of 110 patients, admitted consecutively to one inpatient
psychiatric unit. The HTRS and the PAS were used independently for each patient. Four
PAS items (active affect, passive affect, aggression toward self, and patient confusion) sig-
nificantly predicted 38% of the variance from the HTRS; and three HTRS items (isolation
and withdrawal from relationships, noninvolvement in treatment, and wide variability in
mood) significantly predicted 22% of the residual variance from the HTRS. The identified
PAS and HTRS items help to make visible patient problems associated with nurse-perceived
patient treatment difficulty. This identification is potentially important for both clinical and
political purposes.
Ontario
M5S-3H4
Canada
Keywords: nursing practice, patient assessment, psychiatric staff nurses, patient treatment
difficulties
Accepted for publication: 26 November 1999
Patient Assessment Sheet (PAS). Daily measures of patient
Introduction and literature review
acuity in inpatient psychiatric settings are used for two crit-
ical functions: (1) the assessment of patient problems; and
(2) the determination of nurse staffing needs. These func-
tions may be related because as patient problems increase,
more nurses are required if appropriate levels of care and
treatment are to be provided. Hence any assessment instru-
ment needs to be accurate and effective in assessing patient
problems associated with nurse-perceived patient treat-
ment difficulty in hospital.
The many changes in health care such as the decreased
length of hospitalization (Thomas 1993, Nursing Task
Force 1999), the reduction in the number of hospital beds
with concomitant increase in patient acuity, and the
decreased staffing levels of registered nurses (Huston 1996,
Nursing Task Force 1999) underscore the urgent need for
psychiatric nurses to adequately assess and formally docu-
ment patient problems associated with nurse-perceived
patient treatment difficulty on daily measures, such as the
In this study the Patient Assessment Sheet (PAS), used in
© 2000 Blackwell Science Ltd
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R. F. Chiovitti & R. Gallop
an acute psychiatric hospital setting, was compared to a
published valid measure, the Hospital Treatment Rating
Scales (HTRS) (Colson et al. 1985) for its ability to predict
patient problems associated with nurse-perceived patient
treatment difficulty. According to Colson et al. (1985),
patient treatment difficulty as perceived by psychiatric
clinicians is defined as the clinician’s subjective appraisal
of three variables that include: the patient variable (i.e.
problems exhibited by the patient); the clinician variable
(i.e. nurses’ affective reactions to the patient); and the treat-
ment quality variable (i.e. relationship with the patient,
availability of treatment resources and quality of team
work). In this study, only the patient variable aspect of
nurse-perceived patient treatment difficulty was examined.
This was done because the main objective of this study was
to predict ‘patient problems’ that psychiatric nurses per-
ceived as associated with patient treatment difficulty. The
patient variable aspect of patient treatment difficulty is
defined as the nurse’s subjective appraisal of a patient based
on: (1) the nurse’s overall sense of difficulty in working
with the patient in terms of the treatment difficulty a
patient poses in relation to other patients in the setting; and
(2) specific patient problems that interfere with treatment
(Colson et al. 1985).
resulted in delayed patient recovery (Modestin, Greub, &
Brenner 1986); job dissatisfaction and lowered morale
among staff (Kelly & May 1982, Colson et al. 1985,
1986b; Jones 1986, Modestin et al. 1986, Gallop et al.
1993), premature termination of treatment (Breeze &
Repper 1998) and readmission to hospital or ‘revolving
door’ hospital care (Thomas 1993, p. 698). Thus, when
inadequate identification of patient problems associated
with patient treatment difficulty occurs not only may treat-
ment plans be delayed and inadequate, but also may pose
complications for the patient, the healthcare team, and
hospital resources.
Patient treatment difficulty specific
assessment measures
The literature contains an abundance of instruments used
for patient assessment in psychiatric nursing (Raskin 1982,
Van Riezen & Segal 1988, Savage 1994). However, few
have been shown to be inclusive enough to address patient
problems associated with nurse-perceived patient treat-
ment difficulty. It is suggested that neither the research or
clinical assessment measures (Albiez-Gibbon 1986, Mohr
& Noone 1997), or patient classification or computerized
documentation measures (Cockerill & O’Brien-Pallas
1990, Fagerstrom & Engberg 1998, Fagerstrom, Engberg
& Ericksson 1998) sufficiently capture the complexity of
patient care requirements in terms of patients’ behavioural
problems and interpersonal aspects of care that go beyond
the physical care and biomedical treatment of the patient.
There is a lack of assessment measures that are designed
to assess patient treatment difficulty as a unique phenome-
non. Several assessment measures such as: the Nurses’
Observation Scale for Geriatric patients (Spiegel et al.
1991), the Rating Scale for Aggressive Behaviour in the
Elderly (Patel & Hope 1992), the Nursing Observation of
Behaviour Scales (Brawley, Lancee, Alion, & Brown 1978),
the Nursing Observation of Behaviour Syndromes (Craig
1970), and the Manchester Nurse Rating Scales (Brierley,
Szabi, Rix & Bradshaw 1988) include single behaviours that
might correspond to patient treatment difficulty. However,
these assessment measures have limitations, including a lack
of a conceptual framework for patient treatment difficulty
and an absence of combinations of patient problems that
reflect patient treatment difficulty dynamics.
Patient problems associated with clinician perceived
patient treatment difficulty
A number of investigators have identified patient problems
that predict clinician perceived patient treatment difficulty.
Patient problems predictive of patient treatment difficulty
include withdrawn psychotic behaviours, help rejecting
behaviour, suicidal depressed behaviour, violence agita-
tion or dangerous behaviour, and attention seeking and
manipulative behaviour (Colson et al. 1985, 1986a,
1986b; Colson 1990, Lancee & Gallop 1995). These prob-
lems are found to generate staff affective reactions such as
a sense of provocation, intolerance, anger, frustration,
helplessness, guilt, withdrawal, avoidance, confusion, fear,
and divisions among staff (Gallop, Lancee & Garfinkel
1989). Other reactions include a sense of lack of control,
powerlessness, feeling drained, and in some situations
feeling protective of the patient (Gallop & Wynn 1987,
Colson 1990, Lancee & Gallop 1995, Breeze & Repper
1998). These problems make it difficult for staff to ade-
quately assess patient problems and to effectively commu-
nicate and empathize with patients (Neill 1979, Colson
et al. 1986a; Robbins, Beck, Mueller & Mezener 1988,
Gallop et al. 1989, Gallop, Lancee, & Shugar 1990, 1993,
Gallop & Wynn 1987, Breeze & Repper 1998). Inade-
quate, inaccurate, and/or delayed assessment of patient
problems associated with patient treatment difficulty have
Furthermore, several assessment measures in psychiatric
nursing were designed to assess for the presence of patient
problems associated with psychiatric disorders, and not for
assessing patient treatment difficulty. These include, but
are not limited to the: Brief Cognitive Rating Scale, Beck
Depression Scale, Hamilton Rating Scale for Depression,
Hamilton Anxiety Rating Scale, Folstein Mini-Mental
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Detecting nurse-perceived patient treatment difficulty
State Exam, and Yale-Brown Obsessive Compulsive rating
Scale (Shives 1998).
ples of patient progress items included, but were not
limited to; ate well, voiding adequately, slept, well
groomed, logical, adequate fluids, and settled. These
patient progress items on the PAS are necessary for the
daily assessment of patients in psychiatric settings, but
were not relevant to the objective of this study. The main
objective of this study was to predict patient ‘problems’
that psychiatric nurses perceived as associated with patient
treatment difficulty.
The lack of assessment measures for treatment difficulty
has meant that the studies conducted to evaluate or
compare assessment instruments may have limited applic-
ability to assessing patient treatment difficulty. For
example, the Risk Screening Instrument (RSI) was designed
for assessing suicide and violent behaviour risk for the
psychiatric inpatient setting (Holdsworth, Collis, & Alliot
1999). Although the RSI may capture some of the behav-
iours associated with patient treatment difficulty, the
underlying dynamics of the behaviours on the RSI are
attributed to suicide risk and violent behaviour risk, and
not to patient treatment difficulty. Moreover, Davidhizar
et al. (1991) compared three rating scales used with psy-
chiatric patients. These were: the Nurses’ Observation
Scale for Inpatient Evaluation (NOSIE), the Pardue-Dick
Patient Classification System (PDCS), and the Allen Cog-
nitive Level Test (ACLT). However, each of these three
rating scales were designed for a specific purpose, and not
for assessing patient treatment difficulty. The NOSIE was
designed to evaluate medication effects and behaviours
associated with chronic schizophrenia (Honigfeld & Klett
1965, Davidhizar et al. 1991). The PDCS was designed to
group or to classify patients for treatment and care (Pardue
& Dick 1986, Davidhizar et al. 1991). In contrast, the
ACLT was designed to predict and measure the patient’s
performance in routine activities of daily living (Davidhizar
et al. 1991). Davidhizar et al. (1991) concluded that
‘each of the [three] tools measured different aspects of
patient behaviours and needs . . . [and that] more was
learned about the 60 subjects through the three tools
combined than through any one of them alone’ (p. 24).
Moreover, Davidhizar et al. (1991) reported that ‘in no
area was there enough correlation to suggest that one tool
could be preferred over the other two’ (p. 24).
The Hospital Treatment Rating Scales (HTRS) is a clini-
cal measure that accounts for both patient behavioural
problems and the interpersonal aspects of care. Although,
the HTRS was not specifically designed for daily use, the
different subscales of the HTRS were shown to be com-
prehensive enough to address patient problems associated
with perceptions of patient treatment difficulty in a short
stay acute psychiatric hospital setting, where hospitaliza-
tion stays are 35 days or less (Gallop et al. 1993). It is for
this reason that the HTRS is used to examine the research
questions in this study. The HTRS is a valid instrument for
assessing patient treatment difficulty, but the HTRS was
not substituted for the PAS because the HTRS only con-
tains patient ‘problems’. In contrast, the PAS contains both
patient ‘problems’ (26 items used in this study) and patient
‘progress’ items (22 items not used in this study). Exam-
Research questions and aims of the study
1 How effective is the PAS in predicting the variance in
nurse-perceived overall patient treatment difficulty as
measured by the Overall Extent of Treatment Difficulty
scale (OETDS) of the HTRS in psychiatric patients in
the inpatient hospital setting?
2 Are there critical patient problems missing from the PAS
that can be identified by analysis of the residual variance
of the OETDS?
Methodology
Description of the design, setting and sample
A correlational design was employed in this study. Eight
psychiatric nurses consented to participate, and provided
nurse ratings of 110 consecutively admitted patients. Nurse
ratings were included if the nurse had completed the two
study instruments, the HTRS (Colson et al. 1985) and the
PAS between the 14th and 21st day of hospitalization.
Moreover, if a patient, already in the study, was read-
mitted during the 11-month study period, nurse ratings for
the patient’s readmission hospital stay were not included.
The setting is an acute care 20-bed general psychiatric unit
within a metropolitan teaching hospital. The average
length of hospital stay, during the time of data collection,
was 32 days. This psychiatric unit has approximately 350
admissions a year and within a 6-month period, approxi-
mately 33% of the patients are readmitted once and five
to ten percent are readmitted more than once. The unit is
staffed by eight full time registered nurses, all of whom par-
ticipated and stayed in the study.
Instruments
The Hospital Treatment Rating Scales
The HTRS consists of six subscales. Each of these subscales
has an acceptable level of reliability and validity as docu-
mented by studies (Colson et al. 1985, 1986a; Gallop et al.
1993). For this study, two of the six HTRS subscales were
© 2000 Blackwell Science Ltd, Journal of Psychiatric and Mental Health Nursing 7, 213–220
215

R. F. Chiovitti & R. Gallop
used. These were the Overall Extent of Treatment Diffi-
culty Scale (OETDS) and the Patient Problem List (PPL).
The OETDS renders a single statement score, on a one-
to-six point rating scale, ranging from ‘the treatment of this
patient is relatively free from difficulty’ to ‘treatment dif-
ficulty for this patient is among the most extreme I have
experienced’. The nurses gave a one time rating by select-
ing one of the six statements on the OETDS that reflected
their experience with the patient over the last 21 days. The
interrater reliability of the OETDS is reported to be .80
(Colson et al. 1985, 1986a; Gallop et al. 1993).
The PPL consists of 27 problem areas that might be
descriptive of the patient during hospitalization. Examples
of problems are verbal hostility and anger, excessive or
inappropriate demands, poor impulse control, self abuse,
and wide variability in moods. The PPL asks nurses to rate
the patient for each problem on a one-to-five Likert-
type scale (ranging from ‘does not apply, irrelevant’ to
‘extremely descriptive of person’). Nurses gave a one time
rating by selecting one of the five statements that best rep-
resented their experience with the patient during the last
21 days. The internal consistency reliability of the specific
items from the PPL items was reported to range from .54
to .90 (Colson et al. 1985, 1986a; Gallop et al. 1993). Data
from the OETDS and the PPL from the HTRS were col-
lected during the 14th to 21st day by the patient’s primary
nurse whenever possible. For eight of the 110 HTRS’ com-
pleted, data were provided by the patient’s secondary
nurse.
Data reduction and analysis
In order to compare the one time rating on the OETDS and
the PPL (i.e. nurse asked to reflect on past 21 days with
the patient) with the multiple ratings of the PAS (i.e. nurse
instructed to reflect on each individual day with the
patient), the following procedure was followed for each
item on the PAS list:
1 Any occurrence of a patient problem on the PAS during
a 24 hour period was counted as one occurrence; there-
fore, the range of occurrences for any patient problem
on the PAS was from zero (no occurrence over 21 days)
to 21 (21 occurrences over 21 days);
2 when data collected were less than 21 days (i.e. the
patient was discharged at day 15) a weighted mean item
score was substituted so that a score for 21 days was
calculated for each item.
Two multiple linear regressions were conducted. One
multiple linear regression was used to describe the strength
of PAS items to the prediction of nurses’ OETDS scores
(research question one). The second regression model was
used to describe the strength of PPL items to the predic-
tion of OETDS residual to identify PPL items missing from
the PAS (research question two).
Results
There were 130 psychiatric patients admitted to the general
psychiatric unit during the 11-month data collection
period. Of these patients, 20 patients were not eligible (11
patients were readmissions and no HTRS data were avail-
able for nine patients); and, 110 patients (85.0%) met the
criteria for this study. All eight nurses on the unit consented
to participate in the study and none withdrew from the
study.
The Patient Assessment Sheet
The Patient Assessment Sheet (PAS), developed by the hos-
pital, is a checklist of 26 problem areas reflecting possible
patient problems that can occur during hospitalization.
Examples of problems are disorganization, restless, wide
range mood, aggression to others, and guarded behaviour.
Nurses completed the PAS flowsheet daily as part of their
24 hour assessment of patients. The PAS asks nurses to
place their initials next to the problems that were exhib-
ited by the patient and to place a vertical line in all the
other remaining items that did not apply to the patient.
Regression analyses
Strength of PAS items to predict OETDS
In order to conduct multiple linear regression, several steps
were taken first to reduce the problem of multiple items
and problems of multicollinearity (i.e. situations in which
there is a high correlation between variables). First, only
PAS items (independent variables) significantly correlated
(p ꢀ 0.05) with the OETDS scores (as the dependent vari-
able) were selected for entry (see Table 1).
Table 1 shows that 18 PAS items were significantly cor-
related with the nurses’ OETDS scores. Secondly, exami-
nation of the intercorrelations between these 18 PAS items
showed that certain PAS items (irritable, impulsive, unpre-
dictable as well as withdrawn, isolated, subdued, down-
Data collection procedure
The data for the HTRS questionnaire component (i.e. the
OETDS and the PPL scales) of this correlational study were
derived from a database collected at the setting for a parent
study. The parent study investigated the validity of the
HTRS for use in acute care psychiatric settings (Gallop
et al. 1993). The data from the HTRS and the PAS were
selected to correspond to the same patient assessment
period.
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Detecting nurse-perceived patient treatment difficulty
Table 1
Table 2
PAS items significantly correlated with eight nurses’ OETDS scores
(n = 110 patients)
PAS patient problems attributed to patient treatment difficulty on
OETDS by eight psychiatric nurses (n = 110 patients): Variance
explained in OETDS by PAS
PAS items
Pearson r
p £
Patient problems
Beta
p <
Adjusted R2
1. absence without leave risk
2. aggression to self
3. aggression to environment
4. blunted
5. irritable
6. subdued
7. downcast
8. disorganized
9. guarded
10. impulsive
11. restless
12. unpredictable
13. withdrawn
14. confused
15. vague
16. dishevelled
17. isolated
0.30
0.40
0.23
0.28
0.33
0.23
0.35
0.21
0.22
0.48
0.23
0.37
0.26
0.30
0.27
0.28
0.35
0.24
0.01
0.01
0.05
0.01
0.01
0.05
0.01
0.05
0.05
0.05
0.05
0.01
0.01
0.01
0.01
0.01
0.01
0.05
Active Affect
Group
0.28
0.01
Irritable
Impulsive
Unpredictable
Passive Affect Group
Withdrawn
0.36
0.01
Isolated
Subdued
Downcast
Aggression to self
Confusion
0.25
0.18
0.01
0.05
Optimal Regression
Model (using PAS)
0.38
18. remained on unit
Table 3
PPL items significantly correlated with residual variance in OETDS
(n = 110 patients)
cast) were highly interrcorrelated (r ꢁ 0.7). Before the
regression procedure was conducted, these PAS items of
irritable, impulsive, unpredictable were combined (as a
sum) (Cohen & Cohen 1983) in one index called active
affect, while the PAS items of withdrawn, isolated,
subdued, and downcast were combined (summed) as
passive affect.
A stepwise multiple regression procedure showed that a
group of specific PAS items: active affect, passive affect,
aggression to self, and patient confusion were, as a group,
most predictive of the nurses’ OETDS. These four patient
problem areas in the regression model (based on PAS items)
significantly explained 38% of the variance in the nurses’
OETDS scores (Table 2).
As a further check, the residual of the OETDS was
correlated with all the remaining PAS items not already in
the regression model. There were no more PAS items cor-
related with the residual of the OETDS. Further testing
demonstrated that no alternative model was found that
accounted for, as much, or more than the 38% of the vari-
ance in the nurses’ OETDS scores.
PPL items
Pearson r
p £
1. self destructive behaviour (suicidal intent)
2. verbal hostility and anger
3. dishonesty or antisocial behaviour
4. isolation and withdrawal from relationships
5. psychotic symptomology
6. bizarre, socially inappropriate
7. excessive or inappropriate demands
8. manipulative and controlling behaviour
9. plays one person against another
10. wide variability in moods
0.21
0.21
0.26
0.38
0.24
0.20
0.27
0.33
0.21
0.25
0.21
0.30
0.23
0.25
0.37
0.28
0.27
0.38
0.21
0.05
0.05
0.01
0.01
0.05
0.05
0.01
0.01
0.05
0.01
0.05
0.01
0.05
0.05
0.01
0.01
0.01
0.01
0.05
11. depression
12. agitation and anxiety
13. somatization
14. poor impulse control
15. patient is slow to change
16. actively sabotages treatment
17. regresses after making progress
18. not involved in treatment
19. family blocks the treatment process
20. other treatment difficulties(legal issues,
staff changes, family tragedy)
0.30
0.01
were significantly correlated (p ꢀ 0.05) with the OETDS
residual.
Examination of OETDS residual to identify patient
problems missing from the PAS
The PPL items (independent variables) were entered into
a stepwise multiple linear regression procedure with the
OETDS residual as the dependent variable. Three PPL
items (isolation and withdrawal from relationships, not
involved in treatment, and wide variability in mood shown
in Table 4), significantly (p ꢀ 0.05) explained 22% of the
OETDS residual variance.
There were no more PPL items correlated with the resid-
ual of the OETDS. It should be noted that the PPL item of
‘wide variability in mood’ was significantly correlated with
the OETDS residual (Table 4); yet, a similar sounding PAS
Because the optimal regression model (based on PAS items)
only explained 38% of the variance from the OETDS,
stepwise multiple linear regression was used to examine the
residual variance from the OETDS to see if patient prob-
lems from the PPL of the HTRS could be identified to
explain further, the variance in the OETDS residual. A
similar plan to reduce the difficulty of multiple problems,
and for regression was followed for the PPL items that was
used with the PAS items. Table 3 shows that 20 PPL items
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R. F. Chiovitti & R. Gallop
Table 4
item, ‘wide range mood’, is probably under reported and
perhaps explains why ‘wide range mood’ from the PAS was
not significantly correlated with the nurses’ scores on the
OETDS. This is one example of how the PAS, because it
describes patient problems as unitary events over a short
period of time, does not allow for combinations of patient
problems associated with nurse-perceived patient treat-
ment difficulty to be accurately identified. Similarly,
although the PAS items from the first regression model
(active affect, passive affect, aggression to self, and confu-
sion) account for 38% of variance in OETDS, the items
need to be assessed as one group and not as separate
events, if nurse-perceived patient treatment difficulty is to
be accurately identified. Mohr & Noone (1997) noticed
a similar problem in their exploratory study of clinical
progress notes used for assessment documentation. They
indicated that often in psychiatric settings ‘fragments of
patient behaviour’ (Mohr & Noone 1997, p. 330) are pre-
sented in assessment records, as was the case with the PAS
(i.e. PAS presents patient problems as separate entities).
The inability to accurately identify the combinations of
patient problems on the PAS is problematic since the accu-
rate assessment of patient problems is critical to planning
patient care and determining the nurse staffing needed for
providing appropriate levels of care and treatment. In
contrast, the PPL item, ‘wide variability in mood’ (from
the HTRS) captures the different ranges of patient mood,
allows the nurse to examine patient assessment parameters
in relationship to each other, and allows nurses to assess
for continuous interpersonal patterns over a longer period
of time (i.e. the PPL asks nurses to describe ‘wide vari-
ability in mood’ at one point in time covering a two to
three week period). Mohr & Noone (1997) corroborate
the importance of examining patient behaviours in rela-
tionship to each other in psychiatric settings. They indicate
that assessments of patient ‘behaviour should be inter-
preted in relationship to other behaviours…[and] not focus
exclusively on just a behaviour’ (Mohr & Noone 1997,
p. 329).
Residual variance explained in OETDS by PPL: Patient problems
from PPL not explained by the PAS (n = 110 patients)
Patient problems
Beta
p £
Adjusted R2
Isolation and withdrawal
from relationships
Not involved in treatment
Wide variability in mood
Regression Model
0.25
0.25
0.19
0.01
0.01
0.03
(using PPL items)
0.22
item, ‘wide range mood’, did not emerge as one of the PAS
items significantly correlated with the OETDS.
Discussion, conclusions and limitations
The two regression models in this study demonstrated that
a combination of PAS and HTRS items provide the best
model for predicting patient treatment difficulty. It is not
surprising that nurse-perceived patient treatment difficulty
is predicted by a combination of patient problems, as
opposed to separate patient problems. What these findings
do demonstrate are the risks inherent in simple behavioural
checklists for capturing combinations of patient problems
associated with nurse-perceived patient treatment diffi-
culty. Although a measure such as the PAS captures the
hour to hour behavioural observations of patient problems
from regression model one (active affect, passive affect,
aggression to self, confusion), the PAS fails to capture the
summative effect of combining these four items. The PAS
also fails to capture three items from the PPL (‘isolation
and withdrawal from relationships’, ‘noninvolvement in
treatment’, ‘ wide variability in mood’). The PAS describes
patient problems as unitary events (present or absent), and
as such the risk for overlooking summative effects (dif-
ferent ranges and combinations) of problems and overall
interpersonal processes is high.
The risk as it relates to summative effects of problems is
exemplified by the study finding related to ‘wide range
mood’ item from the PAS as compared to the ‘wide vari-
ability in mood’ from the PPL of the HTRS. The PAS item
‘wide range mood’ was not significantly correlated with the
OETDS, while the similar sounding PPL item, ‘wide vari-
ability in mood’ was significantly correlated with the
OETDS residual. Because on the PAS, ‘wide range mood’,
is described as a unitary event (present or absent), the dif-
ferent ranges and combinations of patient mood are not
captured. This could mean that if a patient exhibits an
elated mood at one time and a depressed mood at another
time, the nurse using the PAS is likely to record these
moods as two separate events, and less likely to record
these as representative of ‘wide range mood’. The PAS
In addition to the above risks associated with overlook-
ing summative effects of patient problems, the risk of over-
looking overall interpersonal processes on the PAS is high.
In hospitals, nurses are in the unique position of being
constantly available, and in close proximity, to patients
remaining on the nursing unit (Larsen & George 1992) yet
assessment parameters on the PAS are not designed to
enable the nurse to formally document the patient’s behav-
iour as occurring in a continuous interpersonal context (i.e.
only detects presence or absence of patient behaviour over
a short period of time). The absence of continuous inter-
personal parameters on clinical measures such as the PAS,
is a serious clinical and political problem in caring for
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Detecting nurse-perceived patient treatment difficulty
patients from a nursing perspective, as the meaning of a
patient’s behaviour in a continuous interpersonal context
is minimized, and at the same time promotes the invisibil-
ity of the nurse’s interpersonal work.
valued quality or skill deserving of recognition’ (Braj 1994,
p. 38).
Although the findings of this study are limited to one
urban site, and may not be generalizable to other acute
hospital settings, the issue still remains that assessment
instruments need to accurately assess and document
patient problems associated with nurse-perceived patient
treatment difficulty. It follows then that future research and
development of clinical assessment and documentation
measures needs to take into account the clinical and po-
litical ‘value’ (as well as the consequences) of including
combinations of patient problems. It is suggested that
nurse-perceived patient treatment difficulty is much more
than assessing unitary patient problems as they relate to
acuity, symptoms, and patient responses to medication, it
is about assessing patient problems, as combinations, as
they relate to the continuous interpersonal context of
nursing care.
Historically, the integrating and co-ordinating functions
of the nurse on the unit, as well as the interpersonal and
emotional work of the nurse have been cited as some exam-
ples of the ‘taken for granted’ aspects of nursing (Larsen
& George 1992, Wolfe 1989, Wolfe 1999a). One of the
clinical consequences of overlooking the continuous
interpersonal context, and specific combinations, patient
problems on clinical measures, such as the PAS, is the emer-
gence of situations in which the health care team may
feel increasingly engulfed and frustrated by patient prob-
lems as separate entities. As demonstrated by the two
regression models built, each patient problem alone is
subtle, but as a group the patient problems identify the
overarching problem of nurse-perceived patient treatment
difficulty. Given the subtlety of the individual patient prob-
lems associated with nurse-perceived patient treatment dif-
ficulty, it is not surprising then that patients linked with
nurse-perceived patient treatment difficulty have been
noted to become ‘victims of a curiously stable breakdown
in communication which in some cases lasted for years’
(Neill 1979, p. 211). It is conceivable that this breakdown
in communication may be perpetuated by daily patient
assessment sheets that do not enable the psychiatric nurse
to adequately assess, identify, and document specific
patient problems in relationship to each other and as a
group. It follows then that interpersonal difficulties may
become chronic. These difficulties are problematic in
view of short hospital stays (Ministry of Health Ontario
1988, 1993a, 1993b; Thomas 1993, Nursing Task Force
1999) and frequent readmissions or ‘revolving door’ hos-
pitalizations of patients (Roy, Williams, & Dickens 1994,
p. 271).
On a political level, clinical measures that are not
designed to account for the interpersonal processes
encountered by nurses may also perpetuate the social and
economic invisibility and obscurity of nurses’ interpersonal
work and roles (Diers 1986, Larsen & George 1992,
Machin & Stevenson 1997). Because managing patient
treatment difficulty issues in terms of the specific group of
problems generated in the regression models are not part
of formal and paid accounts of work (i.e. assessment para-
meters on clinical measures such as the PAS) in the hospi-
tal, nurses may themselves overlook the interpersonal work
they do and nurse-staffing needs may be underestimated.
For example, Braj (1994) and Wolfe (1999b) observed that
nurses often do not speak of the distress that patients mani-
fest and nurses might believe that interpersonal work ‘is
more of a personal choice or obligation rather than a
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