Peplau's Theory of Interpersonal Relations: An Alternate Factor Structure for Patient Experience Data?

This study explores the use of Peplau's middle-range theory of interpersonal relations in nursing to demonstrate nursing's extensive contribution to the experiences of hospitalized patients. A confirmatory factor analysis of data from the responses of 12,436 patients to 16 items on the Consumer Assessment of Healthcare Providers and Systems–Hospital survey was used to test a latent factor structure based on Peplau's middle-range theory of interpersonal relations.

Peplau's Theory of Interpersonal Relations: An Alternate Factor
Structure for Patient Experience Data?
Thomas A. Hagerty, RN; PhD1 [Adjunct Professor], William Samuels, PhD2 [Director of
Assessment and Accreditation], Andrea Norcini-Pala, PhD3 [Postdoctoral Fellow], and
Eileen Gigliotti, RN; PhD4 [Professor]
1LaGuardia Community College, New York, NY, USA
2College of Staten Island, Staten Island, NY, USA
3New York State Psychiatric Institute and Columbia University, New York, NY, USA
4College of Staten Island, Staten Island, NY, USA
Abstract
A confirmatory factor analysis of data from the responses of 12,436 patients to 16 items on the
Consumer Assessment of Healthcare Providers and Systems–Hospital survey was used to test a
latent factor structure based on Peplau's middle-range theory of interpersonal relations. A two-
factor model based on Peplau's theory fit these data well, whereas a three-factor model also based
on Peplau's theory fit them excellently and provided a suitable alternate factor structure for the
data. Though neither the two- nor three-factor model fit as well as the original factor structure,
these results support using Peplau's theory to demonstrate nursing's extensive contribution to the
experiences of hospitalized patients.
Keywords
confirmatory factor analysis; HCAHPS; patient experience; Peplau
Patients' experiences in hospitals are important indicators of the quality of hospital care
(Epstein, Fiscella, Lesser, & Stange, 2010). Patients' experiences are defined as their
perceptions of phenomena for which they are the best or only sources of information, such
as personal comfort or effectiveness of discharge planning. A primary way in which patients'
experiences are measured in the United States (US) is by the Consumer Assessment of
Healthcare Providers and Systems–Hospital (HCAHPS) survey. This survey was created to
facilitate public reporting of patient experience data so that consumers could compare
hospital scores and make informed choices and hospitals could see their strengths and
weaknesses with regard to patients' experiences (Centers for Medicare and Medicaid
Services [CMS], 2012).
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Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the authorship and/or
publication of this review.
HHS Public Access
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Published in final edited form as:
Nurs Sci Q. 2017 April ; 30(2): 160–167. doi:10.1177/0894318417693286.
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Only 4 of the 32 items on the HCAHPS survey explicitly are given the heading: “your care
from nurses.” However, other HCAHPS items arguably reflect the work of nurses and ask
about, for example, how patients' pain was managed, how responsive staff were to requests
for help, environmental quietness and cleanliness, medication teaching, and discharge
planning. These items refer only to “hospital staff,” even though it is likely that patients'
answers largely reflect nurses' contributions to patients' care.
The conceptual framework used in developing the HCAHPS survey is derived from the
Institute of Medicine (IOM). Though a latent structure following the IOM's conceptual
framework should fit most sets of HCAHPS data well, it was hypothesized that a latent
structure reflecting a middle-range nursing theory would provide a comparably good fit to
the data, given the large role nurses play in many aspects of patients' hospital experiences.
Demonstration of a comparable factor structure based on a middle-range nursing theory
would more fully reflect nurses' wide contributions to patients' experiences, as measured by
the HCAHPS survey.
Peplau's (1952/1991/1997) middle-range theory of interpersonal relations in nursing was
chosen as a suitable nursing theory for this research because Peplau frequently
acknowledged the importance of patients' experiences of nursing care. In the theory of
interpersonal relations in nursing, Peplau emphasized patients' experiences and the effect
that nurse-patient relationships have on those experiences. Peplau asserted that the focus of
scientific research in nursing should be patients, their needs, and their perceptions about the
care they received from nurses (Gastmans, 1998). The purpose of this paper is to report the
results of a confirmatory factor analysis done to compare the factor structure of HCAHPS
data using both the IOM (2001) conceptual model and Peplau's middle-range theory of
interpersonal relations in nursing.
Conceptual Frameworks
Institute of Medicine Framework
The conceptual framework for the HCAHPS is guided by Institute of Medicine's (IOM's)
domains of quality healthcare, taken from the 2001 report Crossing the Quality Chasm.
These domains include respect for patients' values and attention to patients' preferences,
expressed needs, physical comfort, and emotional support. The IOM's framework is one that
emphasizes patient-centered care and places patient dignity at the forefront. The HCAHPS
survey has nine underlying factors: (a) communication with nurses (operationalized by
HCAHPS items 1-3), (b) communication with doctors (items 5-7), (c) responsiveness of
hospital staff (items 4 and 11), (d) pain management (items 13 and 14), (e) communication
about medicines (items 16 and 17), (f) discharge information (items 19 and 20), (g) physical
environment (items 8 and 9), (h) transition of care (items 23-25), and (i) overall experience
(items 21 and 22) (Rothman, Park, Hays, Edwards, & Dudley, 2008).
Theory of Interpersonal Relations in Nursing
In Peplau's (1952/1991/1997) theory, nursing is defined as an interpersonal, therapeutic
process that takes place when professionals, specifically educated to be nurses, engage in
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therapeutic relationships with people who are in need of health services. Peplau theorized
that nurse-patient relationships must pass through three phases in order to be successful: (a)
orientation, (b) working, and (c) termination.
During the brief orientation phase, hospitalized patients realize they need help and attempt
to adjust to their current (and often new) experiences. Simultaneously, nurses meet patients
and gain essential information about them as people with unique needs and priorities
(Peplau, 1997). Among the many roles that nurses assume in their interactions with patients,
the first role during the orientation phase is that of stranger. Initially, nurses are expected to
greet patients with the “respect and positive interest accorded a stranger” (Peplau,
1952/1991, p. 44). Patients and nurses quickly pass through this phase and nurses must
continue to display courtesy and respect throughout the three phases. Given that
characteristics of the orientation phase are continued in the other two phases; in the current
study, the orientation phase was not initially hypothesized to be a latent factor.
The next phase is the working phase, which accounts for the majority of nurses' time with
patients. In this phase, nurses make assessments about patients to use during teaching and
when contributing to the interdisciplinary plan of care (Peplau, 1952/1991/1997). During the
working phase, the roles of nurses become more familiar to patients; they begin to accept
nurses as health educators, resource persons, counselors, and care providers. Nurses practice
“nondirective listening” to facilitate patients' increased awareness of their feelings regarding
their changing health (Peplau, 1952/1991, p. 43). Using this therapeutic form of
communication, nurses provide reflective and nonjudgmental feedback to patients for the
sake of helping them clarify their thoughts. In this study, the working phase was
operationalized by measuring the ratings on HCAHPS Items 1, 2, 3, 4, 8, 9, 11, 13, 14, 16,
and 17 (see Figure 1).
The final phase is the termination phase, which is more commonly thought of as discharge
planning (Peplau, 1992). The success of the termination phase is dependent on how well
patients and nurses navigated the orientation and working phases. A major part of the
termination phase occurs when nurses teach patients about symptom management and
recovery at home. In this study, the termination phase was operationalized by measuring the
ratings on HCAHPS Items 19, 20, 23, 24, and 25 (see Figure 1).
Related Literature
Nurses contribute enormously to patients' experiences. The first published, nation-wide
evaluation of the HCAHPS, which included data collected over 1 year (2006-2007) from
2,429 hospitals (with a 36% response rate), found that patients who rated their overall
experiences as most positive were significantly more likely to have had higher numbers of
nurses per patient days (Jha, Orav, Zheng, & Epstein, 2008). Hospital characteristics and
HCAHPS ratings were examined using multivariate regression models that adjusted for
potential confounding variables such as numbers of beds in hospitals or percentages of
patients receiving Medicaid health benefits. The sample was divided into quartiles, and
among the quartile reflecting the lowest ratio of nurses to patients, only 60.5% of patients
reported the highest global ratings. However, among the quartile reflecting the highest ratio
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of nurses to patients, 66.7% reported the highest global ratings category ( p< .001; the exact
value of χ 2 is not reported for this chi-square test).
Likewise, the HCAHPS items reflecting the communication with nurses' factor have been
found to correspond strongly with patients' perceptions about their hospital experiences.
Investigating the relations between putative factors on the HCAHPS survey with overall
patient experience scores, Wolosin, Ayala, and Fulton (2012) found that higher nurse
communication factor scores were significantly related to achieving the highest possible
overall HCAHPS scores (OR = 1.05; 95% CI not provided; p< .001). This study used binary
logistic regression and controlled for age, gender, race, education, preferred language, and
self-reported health status of randomly sampled subjects ( N= 136,546) and had an overall
average response rate of 34%. More recently, a Canadian study that utilized the HCAHPS
survey items with 27,492 discharged, English-speaking patients over a 3-year period found
that of all the HCAHPS factors, the nurse communication factor had the strongest Pearson
correlation with overall experience ratings ( r= .45, p< .001) (Kemp, McCormack, Chan,
Santana, & Quan, 2015). Additionally, it was found that the factors of pain management,
room cleanliness, and room quietness were also significantly related to overall experience
ratings ( r= .31 to .42, p<.001). These factors largely reflect practices under the influence of
nursing.
Two other recent studies have also linked the quality of nursing services with patients'
hospital experiences. The first study, which utilized 2009 to 2011 nurse staffing and patient
experience data from 311 California hospitals, found that higher levels of nurse staffing and
less utilization of per-diem or travelnurses (as opposed to full time staff) are significantly,
positively correlated with better patient experiences (Hockenberry & Becker, 2016). The
second study compared 2010 patient experience data from almost identically matched
Magnet ( n= 212) and non-Magnet ( n= 212) hospitals; patients in Magnet hospitals had
significantly better experiences than those in non-Magnet hospitals (Stimpfel, Sloane,
McHugh, & Aiken, 2016). Due to mandated nurse staffing ratios in the first study and
Magnet designation in the second, two natural experiments occurred that demonstrated
nursing's influence on patient experience.
Methods and Procedures
This study was a secondary data analysis of one hospital system's HCAHPS survey results
using confirmatory factor analyses (CFAs). Confirmatory factor analysis is a type of
structural equation modeling that measures the relation of observed variables (survey items),
known as indicators, to unobserved or latent variables, known as factors. Observed variables
that theoretically should have relations with latent factors will have stronger correlations
than those that theoretically should not have relations (Kääriäinem, Kanste, Pölkki,
Miettunen, & Kyngäs, 2011).
This study consisted of three parts. In Part 1, a CFA tested a model in which patients'
responses to 16 HCAHPS survey items were the observed variables and Peplau's
(1952/1991/1997) working and termination phases were the latent factors. It was
hypothesized that this model would present a significant fit to the data and therefore support
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Peplau's theory about nurse/patient interactions. In Part 2, another CFA tested the fit of the
IOM's established factoral structure with these same 16 HCAHPS survey items. In the third
part of the study, there was a comparison of the model fit indices of these two factor
structures; it was hypothesized that the Peplau-based model would fit the data as well or
nearly as well as the IOM-based model.
Sample
The study sample comprised 15,814 patients, ≥18 years of age, who had at least one
overnight hospital stay and received an HCAHPS survey in 2013. These included patients
discharged to home from the medical-surgical and maternity units of a large, urban, five-
campus academic medical center in the mid-Atlantic region of the Eastern US.
Data Collection
The HCAHPS surveys were administered between 48 hours to 6 weeks after hospital
discharge to a random sample of adult patients with a variety of health problems. The
surveys were sent by mail, and no incentives were offered to subjects for completion. The
surveys were not restricted to Medicare beneficiaries. Subjects were reassured in cover
letters that their participation was voluntary and that participation/non-participation would
not affect their health benefits. They were also reassured of their privacy and were provided
with a toll-free number to call if they had any questions. The study site used a private vendor
approved by CMS to collect the data. More extensive details about the survey protocols for
data collection, coding, and file submission have been published elsewhere (CMS, 2012).
Institutional review board exemption was granted for use of these previously collected and
deidentified data. Data files were kept in a locked office on a single, password-protected
laptop to which only the researcher had access.
Data Analysis
Data were analyzed for completeness and normality using the Statistical Package for Social
Sciences (SPSS) software version 22, and for CFA, data were analyzed using Mplus, version
7.3. Data were analyzed in four steps. Data were first checked for multivariate normality and
missing data. Then, the following CFA fit indices were computed for both the Peplau and
IOM models: the root mean square error of approximation (RMSEA), comparative fit index
(CFI), and Tucker Lewis index (TLI). Finally, the fit indices for both models were compared
using the Bayesian Information Criteria (BIC) approximation and chi-squares.
Results
Return Rate
The rate of survey return was calculated by dividing 80% of the number of patients
discharged from each campus by the number of received surveys per campus (only 80% of
discharged patients are sent surveys). Discharge information was available for only four of
the five campuses, so an overall rate of return could not be estimated. However, the rates of
return for the four campuses ranged from 16.09% to 22.74%. Fifty-eight of the surveys were
from patients who were ≤18 years of age, and these surveys were excluded due to study
delimitations, leaving 15,756 (99.63%).
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Missing Data
Excluding the three items directly related to physicians, there are 16 core items that address
patient experience, two overall hospital ratings items, seven demographic items, and four
items that instruct respondents to skip ahead when indicated and not to answer items about
hospital care they did not receive. The majority of missing HCAHPS answers on Core Items
1-25 in the current study were created by respondents who correctly followed the
instructions to skip items that were not applicable to them; during data analysis, these values
were considered to be missing by design, consistent with CMS guidelines (CMS, 2012).
After recoding and evaluating the missing by design and other missing values, it was found
that 12,436 (78.92%) of the surveys had no missing HCAHPS core data. Of the 3,320
(21.07%) surveys with missing HCAHPS core data, 125 (0.79 %) were found to be missing
answers on 50% or more of these items, and these surveys were discarded according to CMS
guidelines (CMS, 2012).
Little's test was performed on the retained 3,195 (20.27%) surveys to determine if missing
data were missing completely at random (MCAR). The results of the test showed that data
were not MCAR (χ 2 = 17,289.593, df= 103, p< .001). When missing data are not MCAR,
such as in the current study, multiple imputation is an advanced and reliable technique that
should be used to replace the missing data. Although there is no published guidance offered
by CMS regarding the use of multiple imputation for missing HCAHPS data, this has been
the practice of another US health care agency, the Centers for Disease Control, and it was
attempted for this study.
Unfortunately, values for missing data failed to be generated by multiple imputation
attempted using SPSS version 22, Mplus version 7.3, STATA software version 12, and SAS
software version 9.4. To correct for missing data, complete case analysis, also known as
listwise deletion, was used. Although listwise deletion may yield biased parameter
estimates, it is acceptable for use in CFA. After listwise deletion, 78.92% ( N= 12,436) of
the original sample was retained for main analysis. This sample size met the commonly used
criteria of needing >800 subjects to ensure sufficient precision to compare two models of the
same data. Additionally, this sample size was consistent with recommendations for CFAs
conducted using weighted least squares means and variance adjusted estimation.
Demographic Data
Characteristics of the retained sample ( N= 12,436) are reported in Table 1. Mean age was
57.26 years ( SD= 19.03, range = 18-102); 42.36% ( n= 5,268) were men, and 57.64% ( n=
7,168) were women. Mean length of stay (LOS) in the hospital was 4.31 days ( SD= 5.84,
median = 3, range = 1-142); LOS was not normally distributed. Age, sex, and LOS data
were provided by the hospital and are not required by the HCAHPS survey; none were
missing.
The 3,320 (21.07%) deleted surveys showed significant differences on some demographic
variables compared to surveys without missing data. To determine differences, ttests were
used for continuous variables (age, perceptions about physical and mental health, and
educational levels), and cross-tabulation chi-squares were used for categorical variables
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(race and ethnicity, language spoken at home, and LOS). The results showed that patients
whose surveys were deleted due to incompleteness were more likely to be older, with a LOS
of only 1 day, Black or Hispanic or of multiple race, mainly Spanish-speaking at home, less
well-educated, and having lower levels of physical and mental health.
Confirmatory Factor Analyses
Peplau Model—The two-factor Peplau model performed sufficiently well. Factor loadings
were standardized so that loading values could be compared. This was necessary because of
the differing question formats on the HCAHPS survey, where 2, 4, or 11 answers are
possible depending on the question. No outliers—that is very influential items—were found
(Cook's Ds < 1.00; range = 0.0-0.21).
All items loaded rather well onto the factors hypothesized by the Peplau model (see Figure
2). The lowest loading was .490, and the highest was .903. All loadings were statistically
significant at p< .0001. Indicators of model fit for the two-factor structure were acceptable.
The RMSEA was 0.071, 90% CI (0.069-0.072), and the calculated probability of the
population RMSEA to be lower than 0.05 was <0.001. Larger values for RMSEA indicate
worse model fit; ideally, RMSEA values should not be significantly different from zero. An
RMSEA score of 0.01 is considered excellent, 0.05 good, and 0.08 mediocre; the current
score of 0.07 is therefore within the good to mediocre score range. Values larger than 0.10
indicate poorly fitting models, but values from 0.05 to 0.08 represent reasonable errors of
approximation. In addition, models with smaller sample sizes can have artificially large
values for the RMSEA, so the large size of the current sample ( N= 12,436) protected
against inflation of the current RMSEA. The narrow width of the CI indicated that the
RMSEA was accurate.
The CFI was 0.953, above the recommended 0.95 standard for an excellent fit. The TLI was
0.945, below the recommended 0.95 standard for an excellent fit. However, CFI and TLI are
usually considered acceptable when greater than 0.90, and the TLI value of 0.945 was
considered adequate. Thus, the hypothesized two-factor Peplau model produced an
acceptable to good fit to the data.
IOM model—In contrast to the acceptable fit of the Peplau model, the nine-factor IOM
model performed extremely well. As with the Peplau model, all items loaded onto their
anticipated latent factors, and no outliers were identified (Cook's Ds < 1.00; range =
0.0-0.16). In contrast to the mediocre to good score ranges found in the Peplau model,
overall indicators of the nine-factor model fit were excellent. The RMSEA was 0.027, 90%
CI (0.024, 0.028), well below the cutoff of 0.05 for a good model fit. The calculated
probability that the true RMSEA value was <0.05 was 1.00, confirming the strong fit of the
model. The CFI was 0.995, which was above the recommended 0.95 standard for excellent.
The TLI was 0.993, also above the recommended 0.95 standard for excellent.
Formal model comparison—The BIC, which accounts for the number of items in a
model, can be used to compare the relative fit of two models to the exact same data—as was
the case in the current study. The BIC for the Peplau model, 276,596, was slightly larger
than the BIC for the IOM-based model, 270,482, suggesting that the IOM-based model fit
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these data better than the Peplau-based model. The two models were also compared using
log likelihood, which further supported the better fit of the IOM-based model (χ 2 = 129.74,
df= 20, p< .0001).
Ancillary Analyses
In light of these findings and bearing Peplau's original three-phase model in mind,
modification indices (MIs) were inspected to identify adjustments to the two-factor Peplau-
based model that would improve its fit. In particular, correlations between items' residual
variances were considered when theoretically relevant. A correlation between the residual
variances (MI = 750.264) was found between the answers to HCAHPS Item 1 (“During this
hospital stay, how often did nurses treat you with courtesy and respect?”) and Item 2
(“During this hospital stay, how often did nurses listen carefully to you?”). This correlation
was consistent with the orientationphase in Peplau's (1952/1991/1997) original three-phase
theory. It was thus considered that the originally hypothesized two-factor model was
insufficient and that the orientation phase is a stand-alone phase and may not be subsumed
by the other two phases.
The two-factor Peplau-based model was therefore modified to include a third latent factor
(orientation), and a CFA was run on this new model (see Figure 3). The three-factor model
resulted in an improved fit (RMSEA = 0.068 [CI 0.066, 0.069; probability of RMSEA ≤ .05
= 1.00], CFI/TLI 0.958/0.950, χ 2 = 5,879.320, df= 101, p< .0001).
The three-factor model's MIs were then inspected to identify adjustments to the three-factor
model that would improve the fit. Inspection of the MIs revealed relevant relationships
between six items' residual variances: (a) items 13 and 14 (MI = 3,156.404) (pain
management), (b) items 16 and 17 (MI = 716.663) (medication teaching), and (c) items 2
and 3 (MI = 515.364) (nurses listening carefully and explaining). These were the largest
relationships between residuals compared to the remaining correlations (all lower than
339.712). The inclusion of these relationships further improved the fit of the three-phase
Peplau model (RMSEA = 0.039 [CI 0.038, 0.041; probability of RMSEA ≤ .05 ≈ 1.00],
CFI/TLI = 0.986/0.983, χ 2 = 1,975.173, df= 98, p< .0001). As noted previously, a RMSEA
score of 0.01 is considered excellent, 0.05 good, and 0.08 mediocre. The RMSEA score of
0.039 for the three-factor model is within the excellent to good score range of 0.01 to 0.05.
Model comparison—The BIC for the three-factor Peplau model (271,660) was
considerably lower than the two-factor Peplau-based model (276,596; a difference of 4,936).
More tellingly, it was close to the IOM-based model (270,482; a difference of 1,178).
Discussion and Conclusions
This study investigated whether a broader consideration of nursing's contributions to the
experiences of hospitalized patients was supported by conceptualizing items on the
HCAHPS survey through a Peplau-based latent factor structure. It was argued that if the fit
of a Peplau-based model to a large, representative HCAHPS survey data set was comparable
to the fit of the original IOM-based latent factor structure, then this would bolster a broader
consideration of nursing's contributions. The initial, two-factor Peplau-based model did not
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fit the data as well as a three-factor model that included Peplau's orientation phase. In fact,
the three-factor model including the orientation phase fit the data nearly as well as the IOM-
based model and provided a suitable alternate factor structure for the data.
The orientation phase was described by Peplau (1997) as a time for introductions and
listening on the part of nurses: “The orientationof nurse to patient is mostly a one-way
contact: the nurse first identifies herself [sic] by name and professional status and states the
purpose, nature, and time available for the patient … the main focus of the nurse's attention
is on the patient, listening, hearing what is said, and asking who-, what-, where-, when-type
questions to stimulate the patient's descriptions and stories” (Peplau, 1997, p. 164). Peplau
(1992) emphasized that careful, nondirective listening was extremely important and wrote,
“It is during this time period, in the orientation phase, that the nurse's behavior signals a
pattern of receptivity and interest in the patient's concerns or fails in this regard” (Peplau,
1992, p. 164).
In prior research, patients who reported experiencing respect and careful listening by nurses
tended to have more successful transitions from the orientation to the working phase
(Forchuk et al., 1998). Nurses who facilitated a smooth orientation phase for patients were
described by patients as genuine, understanding, and respectful; capable of “treating
[patients] as human beings” (Forchuk et al., 1998, p. 40). Nurses who hindered patients
during the orientation phase were said to be distant, superficial, and arrogant: “They don't
acknowledge me. It's like being in limbo” (Forchuk et al., 1998, p. 41). With regard to
careful listening, one patient stated, “Sometimes it's repetitive and staff tune out. But [my
nurse] continues to listen. That's the difference” (Forchuk et al., 1998, p. 39). Another
patient stated, “She [my nurse] listens to me, what I say. When I talk, she doesn't make a
sound” (Forchuk et al., 1998, pp. 39-40). The HCAHPS survey Item 1 (“During this hospital
stay, how often did nurses treat you with courtesy and respect?”) and Item 2 (“During this
hospital stay, how often did nurses listen carefully to you?”) appear to reflect the orientation
phase. Including them as such helped produce a relatively well-fitting model.
In more recent quantitative research, Otani, Herrmann, and Kurz (2011) found that nursing
care was the most influential factor when tested against staff care, physician care, and
environment. More importantly, Otani and colleagues (2011) found that withinthe nursing
care factor, the first and second most influential empirical variables were answers to
HCAHPS Items 1 and 2. These two items appear to be more empirically linked than was
initially thought in the current study's initial hypotheses. Testing of a more theoretically
accurate three-factor model of Peplau's (1952/1991/1997) theory showed that the three-
factor model was a better fit to the data than the two-factor model. These results supported
Peplau's theory and supported a broader, Peplau-based consideration of nursing's
contributions to the experiences of hospitalized patients, as measured by the HCAHPS
survey.
Applications to Nursing Practice
The professional scope of nursing practice is multidimensional and comprehensive. In their
report on the future of nursing, IOM (2011) noted that “nurses have the opportunity to play a
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central role in transforming the health care system to create a more accessible, high-quality,
and value-driven environment for patients” (p. 85). From the very beginning of the use of the
HCAHPS survey, researchers have found that nursing has more influence than any of the
other factors on patients' overall experiences (Jha et al., 2008). Recent research continues to
support that nursing care is significantly, positively related to many items on the HCAHPS
survey that were not initially framed as being under the purview of nurses, such as discharge
planning and medication communication (Martsolf et al., 2016). The current study
demonstrated that the HCAHPS survey touches on many facets of nursing practice and that
the HCAHPS can be used as a valuable tool to more broadly measure patients' experiences
with nursing care. As the HCAHPS survey represents a way for patients to give feedback on
the quality of their experiences and as a good part of patients' experiences is influenced by
their nurses, hospital leaders may wish to greatly increase nurses' ownership of elements
measured by the HCAHPS survey.
Applications to Nursing Research
Quantitative studies are well-suited for testing middle-range theories of nursing (Fawcett,
2005; Im, 2015). Despite the importance of theory-testing, basic research in this area has
decreased in the last two decades (Im, 2015). This study adds to nursing science by acting as
a test of Peplau's (1952/1991/1997) middle range of interpersonal relations in nursing, a
well-known and seminal theory that despite its age has not undergone much formal testing.
Limitations
The generalizability of the study is primarily limited by the restriction of data to one hospital
system and the response rate (∼20%) to the HCAHPS survey. In addition, the need to
eliminate about 20% of the data due to the missing data further limits some of the
representativeness of the sample.
Acknowledgments
Funding: The authors received no financial support for the authorship and/or publication of this review.
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Figure 1.
Path Diagram of 16 HCAHPS Items That Correspond to Peplau's Phases.
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Figure 2.
CFA Peplau Model.
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Figure 3.
CFA Peplau Model With Three Factors.
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Table 1
Frequency Table—Demographic Variables.
n %
Sex
Male 5,268 42.36
Female 7,168 57.64
Age (in years)
18 to 44 3,743 30.81
45 to 64 3,524 28.34
65 and over 5,169 41.56
Length of hospital stay
≤3 days 7,765 62.44
>3 days 4,671 37.56
Race
White 7,212 57.99
Hispanic 2,087 16.78
Black 943 7.58
Asian 1,021 8.21
Multiple races/ethnicities 139 1.12
Native Hawaiian/Pacific Islander 21 0.17
Native American or Alaska Native 39 0.31
Did not report 974 7.83
Language spoken at home
English 8,884 71.44
Spanish 1,309 10.53
Chinese 342 2.75
Russian 141 1.13
Vietnamese 1 0.008
Other 405 3.26
Did not report 1,354 10.89
Education level
8th grade or less 703 5.65
Some high school, did not graduate 690 5.55
High school graduate or GED 1,828 14.70
Some college/2-year college 2,165 17.41
4-year college 2,455 19.74
More than 4-year college degree 4,022 32.34
Did not report 573 4.61
Admitted through the emergency department
Yes 4,538 36.49
No 7,521 60.48
Did not report 377 3.03
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