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467
Organizational Climate of Staff Working
Conditions and Safety—An Integrative Model
Patricia W. Stone, Michael I. Harrison, Penny Feldman,
Mark Linzer, Timothy Peng, Douglas Roblin,
Jill Scott-Cawiezell, Nicholas Warren, Eric S. Williams
Abstract
Objectives: This project sought to compare measures of organizational climate in
ongoing patient safety studies, identify similarities and setting-specific
dimensions, develop a model of climate domains that are hypothesized to affect
outcomes across settings, and test aspects of the model. Methods: Investigators
who had surveyed health care workers’ perceptions of organizational climate in
six studies funded by the Agency for Healthcare Research and Quality (AHRQ)
were invited to participate. Survey items from each study were classified using
four climate domains found in a prior literature review. The authors discussed
subconstructs, proposed additional constructs, developed an integrative model,
and independently tested selected aspects of the model. Results: The investigators
who participated had studied acute care, home health care, long-term care, and
multiple settings; two investigators had studied primary care. More than 80,000
workers were surveyed. The model’s core climate domains included leadership
(e.g., values) and organizational structural characteristics (e.g., communication
processes and information technology), the impact of which was mediated by four
process variables: supervision, group behavior (e.g., collaboration), quality
emphasis (e.g., patient centeredness), and work design (e.g., staffing). These
factors affect health care worker outcomes (e.g., satisfaction and intention to
leave) and patient outcomes. Overall, the full model explained 24 to 65 percent of
the variance in employee satisfaction, but was not as effective at predicting
intention to leave. Conclusions: While some of these domains appeared in prior
models, new domains—quality emphasis, new subconstructs, information
technology, and patient centeredness—are emerging. Our model invites dialogue
among researchers and informs agenda-setting for future research into
organizational climate and the safety of patients and health care employees. This
integrative model will facilitate cross-study quantification of associations among
variables in these important domains.
Introduction
Three recent reports by the Institute of Medicine (IOM) identified major
safety and quality problems in American health care and drew attention to system-
level sources of these problems.
1–3
As the authors of Crossing the Quality Chasm
stated, “Threats to patient safety are the end result of complex causes … The way
to improve safety is to learn about causes of error and use this knowledge to
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Organizational Climate of Staff Working Conditions and Safety-An
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Advances in Patient Safety: Vol. 2
468
design systems of care so as to … make errors less common and less harmful
when they do occur.”
2
As a result, researchers, policymakers, and health care
providers have intensified their efforts to understand and change organizational
conditions, components, and processes of health care systems as they relate to
safety.
Research studies in health care, along with findings from other industries,
point to a wide range of organizational conditions and work processes that may
shape the performance of health care practitioners and provider organizations.
4–10
Despite the difficulty in implementing far-reaching organizational change, some
health providers have succeeded in restructuring their organizations in ways that
promote quality health care.
11–14
Within this growing body of evidence,
researchers have sought to understand the influence of organizational culture and
climate on health care quality.
Organizational climate refers to member perceptions of organizational
features like decisionmaking, leadership, and norms about work. Organizational
culture refers more broadly to the norms, values, beliefs, and assumptions shared
by members of an organization or a distinctive subculture within an
organization.
15, 16
In the past two decades, many studies of organizational culture
have used standardized questionnaires and cultural inventories, which rely on
members’ perceptions and reports of cultural features.
17–19
Some of these
standardized culture inventories are quite close to the instruments originally
developed for climate studies. Moreover, researchers have sometimes used the
terms “culture” and “climate” interchangeably.
Gradually, evidence is accumulating that links culture and climate to behavior,
attitudes, and motivations among clinicians. These behaviors and orientations can,
in turn, affect quality processes and outcomes. Many studies outside of health care
settings and a growing number of studies in health care, show that employees
have more job satisfaction and experience less stress and burnout when they work
in cultures and climates that have supportive and empowering leadership and
organizational arrangements, along with positive group environments (often
reflecting elements of group support, collaboration, and consensus).
20–24
Furthermore, employee satisfaction and commitment have repeatedly been found
to reduce absenteeism and turnover intentions.
20, 24–26
These findings contain
important implications for health care management. For example, nursing staffs
are more likely to be satisfied, committed, and stable in health care organizations
that support and empower nurses.
1
A more satisfied and stable nursing staff may
more readily contribute to patient satisfaction, help reduce errors, and assist in the
implementation of other steps toward improving health care quality.
21, 27
Studies
outside health care also link satisfaction and commitment to individual
performance and other forms of organizationally constructive behavior.
23, 24
A smaller group of studies explores direct links between culture or climate
and behaviors or outcomes that are related to quality. The dependent variables in
these studies include employee absenteeism, implementation of evidence-based
care management practices, patient satisfaction, and performance.
28–31
However,
solid evidence showing direct impacts of organizational culture or climate on
Modeling Organizational Climate
469
clinical and system outcomes is sparse.
33
Important exceptions include findings of
a positive association between a teamwork-oriented culture and patient
satisfaction in Veterans Health Administration hospitals.
30
Moreover, Clark et al,
report that hospital nurses from units with low staffing and poor organizational
climates (in terms of resources and leadership) were twice as likely as nurses on
well-staffed and better-organized units to report risk factors, needlestick injuries,
and near misses.
33
In instances where culture and climate do not independently
predict clinical and organizational outcomes, they may still act as important
mediating or contextual factors.
29, 34, 35
For example, in Canadian long-term care
facilities, a culture that supports organizational learning and employee
development was found to be a necessary condition for quality improvement
programs to achieve their organizational objectives.
27
Quantitative studies of organizational culture, such as those reported above,
often have drawn on either typological or dimensional models.
19
Typological
models seek to classify entire organizational cultures in terms of a dominant value
or normative orientation. For example, the competing values framework classifies
organizations as predominantly oriented toward internal cohesiveness and human
relations development, creativity and innovation, order and predictability, or
competitiveness and goal attainment.
36
Shortell and his colleagues adopted this
model to the cultures of medical organizations by characterizing the respective
cultural types as group, hierarchical, developmental, or rational in their
orientations.
29, 32
Typological models assume that entire cultures can be
characterized in terms of an overarching substantive theme. In contrast,
dimensional models, including some derived from the competing values
framework, allow for the possibility of internal variations along separate,
conceptually defined orientations.
37
For example, Kralewski, Wingert and
Barbouche developed an instrument for assessing emphasis by members of
medical group practices on each of nine dimensions—innovativeness, group
solidarity, cost-effectiveness orientation, organizational formality, method of cost
control, centralization of decisionmaking, entrepreneurism, physician
individuality, and visibility of costs.
38
Unfortunately, lack of consensus on the key dimensions and subconstructs for
assessing culture and climate has slowed the accumulation of evidence about how
norms, values, and perceptions affect patient safety and other aspects of quality of
care. Investigators in and out of health care have used a very wide variety of
definitions, concepts, measures, and methods to study culture and
climate.
17, 24, 31, 39–41
Although this broad mix of measures and definitions reflects
the complexity of the phenomena under study, lack of definitional and
methodological consistency makes it hard to generalize across studies and
develop evidence-based implications for practice.
This paper reports an effort to help bring order and consistency to this line of
research. In it we develop and test a model of organizational climate in health care
across diverse delivery settings. We focus on organizational climate for a number
of reasons. First, organizational climate features may be more amenable to change
than deep-rooted cultural assumptions and values. Second, the focus on
Advances in Patient Safety: Vol. 2
470
organizational climate, rather than culture, may provide for a better logical fit
between concepts and questionnaire measures than sometimes occurs in
quantitative culture inventories; it seems quite logical that members of an
organization will be aware of their perceptions of organizational conditions
(climate) and will be able to report these perceptions accurately in closed-ended
questions. In contrast, members are less likely to be fully cognizant of shared
norms, values, and basic assumptions, and may face difficulties in characterizing
such complex phenomena in their responses to fixed-choice questions.
42–44
Lastly,
we focus on facets of organizational climate that are particularly relevant to care
providers, health managers, and decisionmakers.
This project resulted from an initiative by the Agency for Healthcare Research
and Quality (AHRQ) targeting the health care workforce and patient safety (RFA
HS01-005). AHRQ sponsored a number of working groups, one of which focused
on working conditions and organizational climate. This working group held a
number of conference calls over a 3-year period to discuss issues developing at
AHRQ, provide an open exchange of ideas regarding the measurement of
organizational climate across health care settings and its relationship to patient
safety, and develop synergy among grantees. Investigators involved in this forum
were invited to participate in this project if they were part of a study team that had
surveyed health care worker perceptions of organizational climate. Based on a
prior literature review and input from the various investigators, the group
discussed conceptual domains and subconstructs of organizational climate related
to perceived working conditions and its relationship to health care worker safety
and patient safety.
18
An integrative conceptual model of organizational climate
was developed by seeking consensus among participants about empirically and
theoretically important constructs.
The integrative model
The integrative model is presented in Figure 1. The model’s core climate
domains include leadership and organizational structural characteristics.
Subconstructs of leadership include organizational values, as well as style and
strategies used by top management. The subconstructs associated with
organizational structural characteristics include formal communication processes,
governance structures, and information technology infrastructure. The direct
impact of these variables on patient and health care worker outcomes is mediated
by four process domains: (1) supervision, (2) group behavior, (3) quality
emphasis, and (4) work design. This model distinguishes between leadership and
supervision. Supervision refers to the direct managers’ style or the recognition an
employee receives on a daily basis. Work design includes five subconstructs: (1)
manageable workload, (2) resources and training, (3) rewards (defined as
monetary compensation such as salary and bonuses), (4) autonomy, and (5)
employee safety. Group behavior includes two subconstructs—collaboration and
consensus (the latter including items such as “there is general agreement on
treatment methods”). Subconstructs associated with quality emphasis include
patient centeredness, patient safety, innovation, outcome measurement, and
Modeling Organizational Climate
471
evidence-based practice. The four process domains influence worker outcomes
and patient outcomes. Finally, worker outcomes are expected to impact upon
patient outcomes.
Methods of validating the model
Although each research team initially conceptualized key relationships among
organizational elements and performance differently, all participating
investigative teams sought to understand essential elements of climate. Therefore,
each investigator provided the health care worker survey items currently being
used in their separate ongoing research projects. An item-by-item analysis of all
surveys was conducted by two of the authors (PS and MH). In this process, the
original climate scales were decomposed, and each item was theoretically
classified using the developed integrative model into the best-fitting domain
and/or subconstruct in the integrative model. For example, items classified as
measuring supervision style include “I feel that I am supervised more closely than
is necessary,” and “a supervisory staff that is supportive of nurses.” A copy of all
final scales is available from the corresponding author.
Reliability statistics (Cronbach’s alpha) of scales were examined and items
were dropped as necessary to develop the most stable measures possible of the
theoretical concepts. Scales that were unstable were dropped from further model
Figure 1. An integrative model of health care working conditions on organizational
climate and safety
Boxes outlined with dotted lines represent domains of organizational climate. Boxes outlined with
solid lines represent outcomes. Core domains are in bold. Subconstructs are bulleted
underneath. The dotted arrows connecting core structural domains represent direct effects on
outcomes, which are mediated by the process domains.
Advances in Patient Safety: Vol. 2
472
testing. All projects were tested for multicollinearity among scales using pairwise
Pearson correlation between scales. Four of the studies found no correlations that
exceeded a cutoff limit of r ≥ 0.60. Two research teams found a correlation over
0.60, and each eliminated one of the pair on this basis. Additionally, one study
examined the collinearity diagnostics included in Statistical Package for the
Social Sciences (SPSS) 11.5 and found levels of collinearity high enough to affect
the models. One scale, with the highest variance inflation factor (VIF), was
eliminated before the final modeling steps. Final models for all studies were thus
free of collinearity levels that would affect model stability.
Because the participating investigators were supplying data from ongoing,
AHRQ-funded patient safety projects, many of the investigators were still in the
process of data collection. Therefore, data on the primary outcome of patient
safety were often not available. Instead, the group members decided to validate
the model using the most common health care worker outcomes found across
studies, which were employee satisfaction and intention to leave.
To test different aspects of the model, each investigative team conducted a
series of similar analyses. First, linear regressions were conducted to investigate
the relationship among the core climate subconstructs of leadership and
organizational structural characteristics. Second, to understand the relationship
among the core climate domains and the four process domains—supervision,
group behavior, quality emphasis, and work design—linear regressions were
conducted using the core domains as the predictor variables and the process
domains as the dependent variables. Third, linear regressions were conducted
using core domains as the independent variables and health care worker outcome
measures as the dependent variables. Finally, investigators tested the independent
effects of each process subconstruct on health care worker outcomes, controlling
for the core domains using multivariate stepwise regressions. In these models, the
core climate subconstructs associated with leadership and organizational
structural characteristics were entered as the first block of independent variables.
Then, the subconstructs associated with the four process domains (supervision,
group behavior, quality emphasis, and work design) were entered as a second
block of independent variables. When investigators found that employee
demographics predicted these outcomes, the demographic variables were
statistically controlled for. It was hypothesized that the independent variables
would be positively related to satisfaction and negatively related to intention to
leave.
There was slight necessary variation in the means used by the investigative
teams to conduct their regressions, due to the nature of secondary data analysis.
Most investigative teams used the subconstructs described as the independent
variables. However, one investigative team combined the subconstructs into
overall organizational climate domains. In another study, intention to leave was
measured as a dichotomous variable, and therefore, a logistic regression was
conducted in a fashion similar to that of the linear regressions.
Modeling Organizational Climate
473
Results
Demographics of participating studies
The six participating investigative teams represent individual studies
conducted across the nation in the following health care settings: acute care (n =
1), home health care (n = 1), long-term care (n = 1), primary care settings (n = 2),
and multiple settings (n = 1). More than 80,000 health care workers were
surveyed in these projects, and employee demographics surveyed by each
investigative team are described in Table 1. Diverse job categories ranging from
certified nursing assistants to hospital administrators and medical assistants to
primary care providers are represented, with the largest sample associated with a
multisite study conducted through the Veterans Health Administration. The
surveys used by each investigative team vary.
Table 1. Description of samples and surveys from each independent project
Study
Characteristic 1 2 3 4 5 6
Description Home care General
medicine
and family
medicine
practices
Primary care
teams
Multiple
settings
across VHA
facilities
32 Colorado
nursing
homes
109
intensive
care units
Description of
sample
Nonclinicians
(M) and
clinicians
(RN, T)
Clinicians
(MD)
Nonclinicians
(R, S) and
clinicians
(LPN, MD,
MA, NP, PA,
RN)
Nonclinicians
(U) and
clinicians (U)
Nonclinicians
(U) and
clinicians
(CNA, RN,
LPN)
Clinicians
(RN)
Final sample
size
952 420 600 74,595 1,763 2,324
Number of
items in survey
99 31 18 29 52 59
VHA = Veterans Health Administration
Nonclinicians include managers (M), receptionists (R), staff (S), and unspecified (U).
Clinicians include certified nursing assistant (CNA), licensed practical nurse (LPN), medical
assistant (MA), medical doctor (MD), nurse practitioner (NP), physician assistant (PA), registered
nurse (RN), therapist (T), and unspecified (U).
Results of empirical testing of the model
The number of applicable items per domain and the reliability of the newly
developed scales from each study are reported in Table 2. All investigative teams,
except that involved with Study 2, were able to develop relatively stable scales at
the subconstruct level. All studies, except that involved with Study 3, had some
type of measure related to the core organizational climate domains. The
“information technology” subconstruct was not represented by an independent
measure in any investigation; however, related items were found in Study 2’s
Advances in Patient Safety: Vol. 2
474
Table 2. Reliability of measures
Study 1 2 3 4 5 6
Core organizational climate domains
Leadership - 2 (.69) - - - -
Values - - - 1 - 2 (.66)
Strategy/style - - - 1 6 (.78) -
Organizational structural
characteristics
- 7 (.76) - - - -
Communication processes 9 (.90) - - - 3 (.70) 2 (.47)†
Governance - - - 2 (.74) 2 (.49)† 4 (.71)
Information technology - - - - - -
Organizational climate process domains
Supervision - - - - - -
Style 7 (.88) - - 4 (.78) 1 4 (.86)
Employee recognition - - - 2 (.70) - 2 (.71)
Work design - 1 - - - -
Manageable workload 5 (.75) - - - - 7 (.72)
Resources/training 7 (.89) - 6 (.74) 2 (.58)† 1 5 (.73)
Rewards 4 (.77) - - - - -
Autonomy 9 (.82) - 7 (.82) - 3 (.24)† -
Employee safety - - - - - -
Group behavior - 9 (.79) - - - -
Collaboration 12 (.89) - 3 (.83) 1 6 (.86) * 3 (.87)
Consensus/harmony - - - 4 (.74) 4 (.78) -
Quality emphasis - 10 (.81) - - - -
Patient centeredness 1 - 2 (.73) 3 (.82) - -
Patient safety 15 (.87) - - - - -
Innovation - - - 2 (.79) - -
Outcome measurement - - - 2 (.68) - -
Evidence-based practice - - - - 1 -
Health care worker outcomes
Satisfaction 12 (.90) 5 (.86) 6 (.87) 4 (.77) - 1
Intention to leave 1 1 0 1 1 1
Note: Each numeral represents of the number of items in the measure. In parentheses is the
Cronbach’s alpha (α) of the scale. Dash (-) represents domain or subconstruct not measured.
*This investigative team had 4 individual scales on collaboration; the number of items and α for
scale 1 is reported in the table. Scale 2 contained 6 items (.80); scale 3 contained 5 items (.80);
scale 4 contained 4 items (.69).
†
Indicates a figure with a Cronbach’s alpha score below the acceptable level (r ≥ 0.60).
organizational structural characteristic scale. Processes related to direct
supervision were measured in four of the studies; however, Study 5 had only one
item in this category. All studies had some measure of work design, with
resources and/or training being the most commonly measured subconstruct. All
studies had some measure of group behavior, and most measured collaboration
(five out of six studies). Study 5, which was conducted in a long-term care setting,
had four separate stable scales of collaboration. Most studies (four of six) had
stable measures of quality emphasis, while Study 5 had a single-item measure.
Employee satisfaction and intention to leave were commonly measured across
studies. Study 6 had a single item related to satisfaction, while the other five
studies had multi-item scales available to measure employee satisfaction. Also in
Study 6, the measure of intention to leave was dichotomous, compared to the
other four studies, which had one-item, continuous-level variables.
Modeling Organizational Climate
475
As predicted, the regression analyses within the separate studies showed there
was a strong relationship among the core climate subconstructs of leadership and
organizational structural characteristics. This analysis was not applicable to Study
3, due to the lack of measurement of core domains. In the other five studies, the
leadership domain or one of its subconstructs significantly (P ≤ 0.05) predicted
measures of organizational structural characteristics; the variance explained
ranged from 24 to 54 percent. The two core domains also significantly predicted
to constructs within the four process domains. Although the number of process
variables varied among the studies, in nearly every case the core domains or their
subconstructs had statistically significant predictions of the process variables. The
core domains also had strong direct effects on the outcome variables. Twenty to
34 percent of the variance in employee satisfaction and 8 to 10 percent of the
variance in intention to leave was explained by the core domains.
Five of the investigative teams were able to conduct the multivariate stepwise
regressions predicting satisfaction (Table 3). Many of the core organizational
climate domains or subconstructs (five of eight) continued to have statistically
significant (P < 0.05) independent effects on satisfaction. In addition, most of the
process domains or subconstructs (25 of 30) had significant independent effects
on satisfaction. Overall, the full model explained 24 to 65 percent of the variance
in employee satisfaction.
Results from the multivariate stepwise regressions predicting intention to
leave are displayed in Table 4. While the direction of the relationships were as
predicted and negative perceptions of organizational climate predicted intention to
leave, only three of the six leadership items had a statistically significant
independent effect on intention to leave, and none of the organizational structural
characteristics (n = 5) was an independent significant predictor. Of the process
domains and subconstructs, supervision had the most consistent independent
significant effect on intention to leave. Overall, the model explained 8 to 23
percent of the variance in intention to leave.
Discussion
This paper presents a model of organizational climate, which encompasses
variables and concepts found in six independent studies. These studies were
conducted across a broad range of settings and surveyed a wide range of health
care workers. We present a preliminary empirical validation of the model by
reporting conceptually plausible associations among the model’s domains and
showing that variables from these domains predict employee satisfaction and
turnover intention in ways that are consistent with previous research. Across
studies, similar patterns of relationships were found. Moreover, the full model
was a better predictor of the outcome variables than were the elements within the
model.
As might have been anticipated from the literature, the climate measures
predicted satisfaction more strongly and more consistently than they predicted
turnover intention. Turnover intentions are subject to many influences exogenous
Advances in Patient Safety: Vol. 2
476
Table 3. Results of multivariate regressions predicting employee satisfaction
Study 1* 2 3 4* 6
Core organizational climate domains
Leadership - 0.14
†
- - -
Values - - - n.s. 0.05
‡
Strategy/style - - - 0.05
†
-
Organizational structural characteristics - 0.15
†
- - -
Communication processes n.s. - - n.s.
Governance - - - - 0.41
‡
Information technology - - - - -
Organizational climate process domains
Supervision - - - - -
Style 0.09
‡
- - 0.06
†
0.67
‡
Employee recognition - - - 0.04
†
n.s.
Work design - n.s. - - -
Manageable workload 0.30
‡
- - 0.04
†
0.04
‡
Resources/training 0.22
‡
- 0.38
†
0.09
†
0.06
‡
Rewards 0.13
‡
- - 0.05
†
-
Employee safety - - - 0.07
†
-
Autonomy 0.07
‡
- - 0.14
†
-
Group behavior - 0.27
‡
- -
Collaboration 0.12
‡
- 0.29
†
0.09
†
n.s.
Consensus/harmony - - - 0.05
†
-
Quality emphasis - n.s. - - -
Patient centeredness - - 0.13
†
0.31
†
-
Patient safety 0.16
‡
- - - -
Innovation - - - n.s. -
Outcome measurement - - - 0.06
†
-
Evidence-based practice - - - - -
R
2
0.57 0.24 0.40 0.58 0.65
Note: Dash (-) represents subconstruct not measured and/or scale not stable enough to be
included in model; n.s. equals not significant results; other results reported are standardized beta
coefficients.
*Models adjusted for age, race, and/or gender.
†
P ≤ 0.01,
‡
P ≤ 0.001
to the realm of climate, such as labor market conditions, assessments of
employability, family status, and career stage.
45
The most important contribution of this study is its climate domains and
subconstructs, which can provide the basis for future studies in health care
settings. The use of this model in future research will promote consistency across
settings and studies, thereby facilitating an accumulation of research findings and
evidence-based recommendations. Further development of operational definitions
and generalizable measures applicable to the model is warranted and invited.
An additional contribution of the model lies in its elaboration of subconstructs
within the domain of organizational structure; these are particularly important for
research on patient safety and health care quality. Information technology, for
example, is an increasingly prominent feature of organizational structure, which
holds substantial promise for health quality.
46
Perceptions of the uses of
information technology in health care organizations may affect the ways that
clinicians respond to information technology innovations.
47
Hence, technology
Modeling Organizational Climate
477
Table 4. Results of multivariate regressions with intention to leave as dependent
variable
Study 1* 2 4* 5* 6
†
Core organizational climate domains
Leadership - -0.13 - - -
Values - - -0.03 -0.12 n.s.
Strategy/style - - n.s. n.s. -
Organizational structural characteristics - n.s. - - -
Communication processes n.s. - - n.s. n.s.
Governance - - - - n.s.
Information technology - - - - -
Process organizational climate domains
Supervision - - - -
Style -0.09 - -0.08 n.s. 1.1
Employee recognition - - -0.02 - n.s.
Work design - n.s. - - -
Manageable workload -0.13 - -0.06 - n.s.
Resources/training n.s. - n.s. n.s. n.s.
Rewards n.s. - -0.01 - -
Autonomy - - -0.06 - -
Employee safety -0.83 - - - -
Group behavior - -0.15 - - -
Collaboration -0.10 - -0.04 -0.53‡ n.s.
Consensus/harmony - - n.s. n.s. -
Quality emphasis - n.s. - - -
Patient centeredness - - -0.12 - -
Safety n.s. - - - -
Innovation - - n.s. - -
Outcome measurement - - -0.02 - -
Evidence-based practice - - - n.s. -
R
2
0.18 0.08 0.15 0.23 -
Note: n.s. equals not significant standardized beta coefficients or odds ratios. Dash (-) represents
subconstruct not measured and/or scale not stable enough to be included in model. All
coefficients and odds ratio reported in table are statistically significant (P ≤ 0.05).
*Models adjusted for age, gender, and/or race.
†
Intention to leave was a dichotomous variable in this study. Therefore, the results from this
investigative team are based on a logistic regression, and odds ratios are presented.
‡
Investigative team had 4 individual scales on collaboration; standardized beta coefficients for
scale 1 is reported in table. The standardized beta coefficient for scale 2 was -0.14, for scale 3 it
was not significant, and for scale 4 it was -0.19.
perceptions are likely to mediate between the introduction of information
technologies and their outcomes. Because of its importance, we included the
technology climate in our model, even though it was not well represented in our
original research studies.
Our model also calls attention to the importance of the climate for quality,
which we labeled “quality emphasis.” Our model specifies the climate for quality
as including the degree to which the delivery organization’s climate is patient-
centered, encourages safety awareness and practices, fosters innovation, and
sustains the use of evidence-based medicine. As other researchers have suggested,
there may be multiple climates within an organization in areas such as safety,
service, or innovation.
35–48
These substantive climates are likely to affect closely
related attitudes and behaviors even more powerfully than abstract climate
Advances in Patient Safety: Vol. 2
478
features such as cohesion or climate strength.
29
Only 2 of the 13 instruments for
assessing culture and climate cited in a recent review contain measures related to
quality climate, and none refers explicitly to an information technology
climate.
19, 49
Due to divergent climate measures in the six studies reported here, the validity
and generalizability of our findings may be limited. Additionally, although this
project is an exemplar of collaboration and resulting synergy, the separate
investigative teams were not yet ready to pool the data into a single database that
would be amenable to analysis through structural equation modeling. Although
we have explored linear relations between climate and other variables, researchers
would be well advised to look closely at nonlinear and noncausal relations. For
example, very negative climates might affect performance, while other climates
do not. In addition, climate may act as a contextual or mediating variable, rather
than a direct cause of important outcomes. Finally, two scales constructed in these
secondary analyses had lower Cronbach’s alphas than often considered desirable.
Given the multileveled and multidimensional nature of organizational climate,
the search for a single instrument—or even a single methodology—is not always
wise.
19, 44
If an organization is considering the implementation of a new
computerized order entry system, for example, investigators may need to
understand only the employees’ perception of information technology and
innovation, not leadership values and styles of supervision. Nonetheless, some of
the measures within our core set of concepts of organizational climate in health
care settings are likely to be applicable to a range of health delivery settings.
Moreover, they may be shown to possess sufficient predictive validity to justify
their routine inclusion in investigations of the causes of outcomes like patient
safety.
Implications for policy and practice
Development and validation of a core set of concepts and measures for
studying climate in health care will permit comparisons across delivery settings
and facilitate development of evidence-based recommendations about human
resource management and organizational design within health services settings.
Databases containing climate measures are already in use in some systems, like
Kaiser Permanente.
50
Moreover, many acute care hospitals are contributing data
to the National Database of Nursing Quality Indicators (NDNQI), which has
adapted measures of nurse perceptions regarding work environment and job
satisfaction.
51
Outside of health care, many government agencies use standardized
climate assessments for benchmarking purposes.
52
Adoption of standardized
climate tools and the creation of databases that support analyses at various
organizational levels will help health care managers to better track their
organization’s progress through time, assess impacts of organizational and
technological changes, and compare the climate in their unit or organization with
those in comparable organizational settings.
It is our hope that the model presented here will encourage researchers to
further refine this core set of concepts and develop standard measures for studying
Modeling Organizational Climate
479
climate in health care as it relates to safety. Standardization of climate measures
will aid in the development of evidence-based recommendations for health
services organization and human resource management within health delivery
settings and perhaps facilitate the ultimate goal of turning results into evidence-
based management practices. The model needs further testing using patient safety
as the primary outcome to aid in this process.
Acknowledgments
The authors wish to thank Pam Owens and Ronda Hughes, both from AHRQ,
who originally participated in the working group’s discussions.
Author affiliations
Columbia University School of Nursing (PWS). Visiting Nurse Service of New York (PF, TP).
Agency for Healthcare Research and Quality (MH). University of Wisconsin (ML). Kaiser Permanente
Georgia (DR). Sinclair School of Nursing, University of Missouri–Columbia (JSC). University of
Connecticut Health Center (NW). Culverhouse College of Commerce and Business Administration,
University of Alabama (EW).
Address correspondence to: Patricia W. Stone, Ph.D., Columbia University School of Nursing, 617
W. 168th Street, New York, NY 10032. Phone: 212-305-1738; fax: 212-305-6937; e-mail:
Ps2024@columbia.edu.
References
1. Page A, editor. Keeping patients safe: transforming
the work environment of nurses. Institute of Medicine.
Washington, DC: National Academy Press; 2004.
2. Institute of Medicine Crossing the quality chasm: a
new health system for the 21st century. Washington,
DC: National Academy Press; 2001.
3. Kohn LT, Corrigan JM, Donaldson MS, editors. To err
is human: building a safer health system. A report of
the Committee on Quality of Health Care in America,
Institute of Medicine. Washington, DC: National
Academy Press; 2000.
4. Aiken L, Clarke S, Sloane D. Hospital staffing,
organization, and quality of care: cross-national
findings. International Journal of Quality in Health
Care 2002;14(1):5–13.
5. Aiken L, Clarke S, Sloane D, et al. Hospital nurse
staffing and patient mortality, nurse burnout, and job
dissatisfaction. JAMA 2002;288(16):1987–93.
6. Hickman DS, Severance S, Feldstein A, et al. The
effect of health care working conditions on patient
safety. Rockville, MD: Agency for Healthcare
Research and Quality; 2003.
7. Hoff T, Jameson L, Hannan E, et al. A review of the
literature examining linkages between organizational
factors, medical errors, and patient safety. Medical
Care Research and Review 2004;61(1):3–37.
8. Krein S, Hofer T, Kerr E, et al. Whom should we
profile? Examining diabetes care practice variation
among primary care providers, provider groups, and
health care facilities. Health Services Research
2002;37(5):1159–80.
9. Reason J. Managing the risks of organizational
accidents. Aldershot, UK: Ashgage; 1997.
10. Schnelle J, Simmons S, Harrington C, et al.
Relationship of nursing home staffing to quality of
care. Health Services Research 2004;39(2):225–50.
11. Scott T, Mannion R, Davies H, et al. Implementing
culture change in health care: theory and practice.
International Journal for Quality in Health Care
2003;15(2):111–18.
12. Aiken L. Measuring organizational traits of hospitals:
the revised nursing work index. Nursing Research
2000;49:146–53.
13. Kizer K. The “New VA”: A national laboratory for
health care quality management. American Journal of
Medical Quality 1999;14(1):3–20.
Advances in Patient Safety: Vol. 2
480
14. Ashish K. Effect of the transformation of the Veterans
Affairs health care system on the quality of care.
NEJM 2003;348:2218–27.
15. Schein E. Organizational culture and leadership. San
Francisco: Jossey-Bass; 1985.
16. Martin J. Cultures in organizations: three perspectives.
New York: Oxford; 1992.
17. Ashkenasy N, Wilderom C, Peterson M, editors.
Handbook of organizational culture and climate.
Thousand Oaks, CA: Sage; 2000.
18. Gershon R, Stone P, Bakken S, et al. Measurement of
organizational climate and culture. Journal of Nursing
Administration 2004;34:33–40.
19. Scott T, Mannion R, Davies H, et al. The quantitative
measurement of organizational culture in health care:
a review of the available instruments. Health Services
Research 2003;38(3):923–45.
20. Shader K, Broome M, Broome C, et al. Factors
influencing satisfaction and anticipated turnover for
nurses in an academic medical center. Journal of
Nursing Administration 2001;31(4):210–16.
21. Tzeng HM, Ketefian S, Redman RW. Relationship of
nurses’ assessment of organizational culture, job
satisfaction, and patient satisfaction with nursing care.
International Journal of Nursing Studies 2002;39:79–
84.
22. Harmon J, Scotti D, Behson S, et al. Effects of high-
involvement work systems on employee satisfaction
and service costs in veterans’ health care. Journal of
Healthcare Management 2003;486:393–406.
23. Carr J, Schmidt A, Ford K, et al. Climate perceptions
matter: a meta-analytic path analysis relating molar
climate, cognitive and affective states, and individual-
level work outcomes. Journal of Applied Psychology
2003;88(4):605–19.
24. Parker C, Baltes B, Young S, et al. Relationships
between psychological climate perceptions and work
outcomes: a meta-analytic review. Journal of
Organizational Behavior 2003;24(4):389–416.
25. Rhoades L, Eisenberger R. Perceived organizational
support: a review of the literature. Journal of Applied
Psychology 2002;87(4):698–714.
26. Gifford BD, Zammuto RF, Goodman EA. The
relationship between hospital unit culture and nurses
quality of work life. Journal of Healthcare
Management 2002;47(1):13–25.
27. Rondeau KV, Wager TH. Organizational learning and
continuous quality improvement: examining the
impact on nursing home performance. Healthcare
Management Forum 2002;15(2):17–23.
28. Eriksen W, Bruusgaard D, Knardahl S. Work factors
as predictors of sickness absence: a three month
prospective study of nurses’ aides. Occupational and
Environmental Medicine 2003;60(4):217–78.
29. Shortell S, Zazzali J, Burns L, et al. Implementing
evidence-based medicine: the role of market pressures,
compensation incentives, and culture in physician
organizations. Medical Care 2001;39(7—Physician-
System Alignment supplement):I62–I78.
30. Meterko M, Mohr D, Young G. Teamwork culture and
patient satisfaction in hospitals. Medical Care
2004;42(5):492–8.
31. Scott T, Mannion R, Marshall M, et al. Does
organisational culture influence health care
performance? A review of the evidence. Journal of
Health Service Research and Policy 2003;8(2):105–
17.
32. Shortell S, Jones R, Rademaker A, et al. Assessing the
impact of total quality management and organizational
culture on multiple outcomes of care for coronary
artery bypass graft surgery patients. Medical Care
2000;38(2):207–17.
33. Clarke S, Sloane D, Aiken L. Effects of hospital
staffing and organizational climate on needlestick
injuries to nurses. American Journal of Public Health
2002;92(7):1115–19.
34. Hofmann DA, Morgeson FP, Geras SJ. Climate as a
moderator of the relationship between leader-member
exchange and content specific citizenship: safety
climate as an exemplar. Journal of Applied
Psychology 2003;88:170–8.
35. Probst TM. Safety and insecurity: exploring the
moderating effect of organizational safety climate.
Journal of Occupational Health Psychology
2004;9(1):3–10.
36. Cameron K, Freeman S. Cultural congruence strength
and types: relations to effectiveness. Research in
Organizational Change and Development 1991;5:23–
58.
37. Quinn RE, Speitzer G. The psychometrics of the
competing values culture instrument and an analysis
of the impact of organizational culture on quality of
life. Research in Organizational Change and
Development 1991;5:115–42.
38. Kralewski J, Wingert T, Barbouche M. Assessing the
culture of medical group practices. Medical Care
1996;34(5):377–88.
39. Martin J. Organizational culture: mapping the terrain.
Thousand Oaks, CA: Sage; 2002.
40. Denison D. What IS the difference between
organizational culture and organizational climate? A
native’s point of view on a decade of paradigm wars.
Academy of Management 1996;21:619–54.
41. Harrison M. Diagnosing organizations: methods,
models, and processes. 3rd ed. Thousand Oaks, CA:
Sage; 2004.
42. Harrison M, Shirom A. Organizational diagnosis and
assessment: bridging theory and practice. Thousand
Oaks, CA: Sage; 1999.
Modeling Organizational Climate
481
43. Martin J, Frost P. The organizational culture wars
game: a struggle for intellectual dominance. In: Clegg
S, Hardy C, Nord W, editors. Handbook of
organization studies. London: Sage; 1996. pp. 599–
621.
44. Rousseau D. Assessing organizational culture: the
case for multiple methods. In: Schneider B, editor.
Climate and culture. San Francisco: Jossey-Bass;
1990. pp. 153–92.
45. Williams ES, Konrad TR, Scheckler WE, et al.
Understanding physicians’ intentions to withdraw
from practice: the role of job satisfaction, job stress,
mental and physical health. Health Care Manage Rev.
2001;26(1):7–19.
46. Bates D, Gawande A. Improving safety with
information technology. N Eng J Med
2003;348(25):2526–34.
47. Darr A, Harrison M, Shakked L, et al. Physicians’ and
nurses’ reactions to electronic medical records:
managerial and occupational implications. Journal of
Health Organization and Management
2003;17(5):349–59.
48. Schneider B, Bowen D, Ehrart M, et al. The climate
for service. In: Ashkanasy N, Wilderom C, Peterson
M, editors. Handbook of organizational culture and
climate. Thousand Oaks, CA: Sage; 2000. pp. 21–36.
49. Scott T, Mannion R, Davies H, et al. Healthcare
performance and organisational culture. Abingdon,
Oxon, UK: Radcliffe Medical Press; 2003.
50. Kam S, Sincere F. Using information from linkage
research studies to improve organizational
performance. The Permanente Journal 1999;3(3):54–7.
51. Gallagher RM, Rowell PA. Claiming the future of
nursing through nursing-sensitive quality indicators.
Nurs Adm Q 2003;27(4):273–84.
52. Muldrow B, Schay B, Buckley T. Creating high
performing organizations in the public sector. Human
Resource Management 2002;41(3):341–54.
